Shriyansh is available for hire
Hire ShriyanshShriyansh Agrawal
Verified Expert in Engineering
Lead Developer
New Delhi, Delhi, India
Toptal member since March 31, 2022
Bio
Shriyansh is a senior AI/ML and platform engineer building production AI systems, LLM applications, large-scale data pipelines, and cloud data platforms (Snowflake, AWS, GCP), with strong data modeling, semantic layers, and documentation. He has worked with companies including Westfield Capital, Graviton Research Capital, and Cal.net, delivering scalable, reliable solutions across machine learning, data engineering, and back-end systems.
Portfolio
Shellx
Amazon Bedrock AgentCore, Oracle Integration Cloud (OIC)...
Spencer Scalzitti
Python 3, MongoDB, Node.js, Informatica, Web Scraping, PostgreSQL, Snowflake
Westfield Capital Management Company, L.P.
Data Engineering, JavaScript, Python, Snowflake, Amazon Web Services (AWS)...
Experience
- Python - 8 years
- Big Data - 6 years
- Artificial Intelligence (AI) - 6 years
- Data Science - 6 years
- Amazon Web Services (AWS) - 6 years
- SQL - 5 years
- Bash Script - 5 years
- Data Engineering - 5 years
Work Experience
Senior AI Solutions Architect
2026 - 2026
Shellx
- Architected an enterprise AI automation platform integrating AWS Bedrock AgentCore with Oracle Integration Cloud to automate order processing, invoice validation, customer support, and ERP workflows.
- Orchestrated AI agents to retrieve enterprise knowledge, invoke business APIs, validate documents, and execute multi-step approval workflows across Oracle ERP, CRM, and warehouse systems.
- Accelerated order processing by automating quote-to-cash workflows, enabling faster revenue recognition and reducing sales cycle delays.
- Architected multi-agent workflows using AWS Bedrock AgentCore and integrated Oracle Integration Cloud with Oracle ERP, REST APIs, and event-driven workflows.
- Reduced customer response times through AI-powered support agents, improving customer satisfaction and increasing retention.
- Designed a secure AI tool-calling system with approval workflows and audit logging.
- Enabled 24×7 AI-driven customer interactions, allowing the business to scale without proportional increases in support staff.
- Automated lead qualification and CRM updates, allowing sales teams to focus on high-value opportunities.
- Eliminated manual document validation through AI-driven invoice and purchase order processing.
Technologies: Amazon Bedrock AgentCore, Oracle Integration Cloud (OIC), Agile Software Development, Large Language Models (LLMs), Generative Artificial Intelligence (GenAI), Apache Kafka, Streaming, AI Agents, AgentGPT, Semantic Search, Jira, Product Strategy
AI & Data Engineering Consultant
2025 - 2026
Spencer Scalzitti
- Designed and delivered an AI-powered property intelligence platform that transformed ownership discovery, buyer identification, and due diligence for commercial real estate investments.
- Architected an intelligent ownership discovery platform that resolved complex ownership structures across South Florida commercial real estate, dramatically accelerating property acquisition research.
- Engineered a graph-based entity resolution system connecting parcels, businesses, trusts, LLCs, and individuals, uncovering hidden ownership relationships that traditional property searches could not identify.
- Developed scalable ETL pipelines that continuously synchronized public records and third-party datasets, ensuring accurate ownership lineage and near real-time property intelligence.
- Built a buyer intelligence engine that matched prospective investors with commercial properties using ownership networks, transaction history, and investment patterns, streamlining acquisition outreach.
- Automated large-scale data collection from public and proprietary sources—including county records, business registries, and government databases—eliminating manual research and significantly increasing data coverage.
- Delivered production systems that directly supported live commercial real estate transactions by enabling ownership verification, buyer discovery, and accelerated due diligence workflows.
- Engineered distributed web crawlers processing millions of public property and ownership records, creating one of the most comprehensive ownership intelligence datasets for South Florida.
Technologies: Python 3, MongoDB, Node.js, Informatica, Web Scraping, PostgreSQL, Snowflake
Data Engineering Lead
2024 - 2026
Westfield Capital Management Company, L.P.
- Led the design and optimization of cloud-native data platforms supporting investment research, portfolio analytics, and executive decision-making across the organization.
- Designed reusable Snowflake data models and centralized business logic that eliminated duplicate SQL across dashboards, reducing maintenance effort while improving reporting consistency.
- Established documentation standards for KPIs, semantic models, and data governance, improving collaboration across engineering, analytics, and investment teams while reducing long-term operational risk.
- Architected an end-to-end cloud analytics platform on AWS, GCP, Snowflake, Sigma, and Power BI, enabling portfolio managers and executives to make faster, data-driven investment decisions.
- Engineered scalable ETL pipelines that ingested and validated daily FactSet market and fundamentals data, automating incremental processing and significantly improving data reliability for downstream analytics.
- Optimized large-scale analytical queries through efficient Snowflake modeling, partitioning strategies, and join optimization, improving scalability while lowering warehouse compute utilization.
- Spearheaded migration of complex analytical workloads from BI tools into Snowflake, substantially improving dashboard performance and reducing front-end query execution times.
- Developed parameter-driven semantic datasets supporting dynamic portfolio analysis across multiple benchmarks, sectors, date ranges, and investment strategies without requiring dashboard redesign.
- Built executive reporting solutions covering portfolio attribution, sector exposure, benchmark-relative performance, capital flows, AUM tracking, and KPI monitoring, providing leadership with near real-time investment insights.
Technologies: Data Engineering, JavaScript, Python, Snowflake, Amazon Web Services (AWS), Azure, Google Cloud Platform (GCP), APIs, Data Warehousing, DevOps, Informatica, Architecture, Product Management, Jira, Product Roadmaps, Product Strategy, SQL, Data Modeling
Software Architect
2024 - 2025
Metafushko
- Led the architecture and delivery of a Web3 ecosystem built on the TON blockchain, combining Telegram Mini Apps, smart contracts, NFTs, and decentralized finance to drive user engagement and token adoption.
- Managed a cross-functional engineering team while designing scalable blockchain infrastructure, automated reward systems, and production-ready back-end services for a rapidly growing crypto community.
- Architected a Telegram Mini App delivering a seamless tap-to-earn experience with integrated TON wallet support.
- Engineered automated Jetton reward distribution, eliminating manual token operations and improving operational efficiency.
- Developed secure smart contracts powering token issuance, reward distribution, and NFT-based engagement mechanisms.
- Built a decentralized AMM protocol across TON and Solana, enabling liquidity management and token swaps for the Aria Coin ecosystem.
- Led a team of 5 engineers, driving architecture decisions, sprint planning, and production releases.
- Designed scalable back-end services integrating blockchain infrastructure with modern APIs and cloud-native applications.
- Collaborated with product and community teams to launch gamified engagement features that increased user participation and ecosystem adoption.
Technologies: Node.js, React, Python 3, Solidity, The Open Network (TON), Back-end Development, Informatica, Architecture, Semantic Search, Product Management, Jira, Product Strategy
Data Scientist
2024 - 2024
Moss & Associates - Main
- Built LLM-powered domain assistants and search indexes specialized in distinct areas of Moss projects, ensuring they provide expert-level knowledge and bot assistance.
- Incorporated AI assistants acting as subject matter experts (SMEs) for various functions and domains within MossOps to offer precise and contextually relevant support.
- Embedded the Knowledge Base and AI assistants within the system’s chat interface, ensuring easy access and interaction for users seeking information or assistance.
- Designed the Databricks Vector Search database based on OpenAI embeddings on the company's unstructured and structured data.
- Developed multiple ETLs with automated orchestration workflow to feed in data from various data sources (internal and 3rd-party providers).
- Ensured the system's architecture was scalable and maintainable, enabling easy updates, expansions, and integration of new knowledge areas and functionality.
Technologies: Data Science, Databricks, Generative Artificial Intelligence (GenAI), OpenAI GPT-4 API, Monitoring, OpenTelemetry, Azure Databricks, Azure Data Factory (ADF), Prompt Engineering, Retrieval-augmented Generation (RAG), Azure Machine Learning, Azure OpenAI Service, Back-end Architecture, Back-end Development, Informatica, Architecture, Product Management, Jira, Product Strategy
Plone Expert (via Toptal)
2024 - 2024
Office Information Systems C-Corp
- Integrated an MFA app (Google Authenticator) to the client's financial website. This served as a secure layer for the client's financial information on the app.
- Designed the functionality of bulk data import and export to facilitate bulk changes for a non-techy administrator of the client's app.
- Oversaw the production and staging layer of the app and servers to improve the CI/CD layer for other developers.
Technologies: Plone, Python, Twilio, API Design, Marketplaces, Monitoring, Back-end Development, Jira, Product Strategy
Data Scientist | Data Engineer (via Toptal)
2023 - 2024
Cal.net, Inc.
- Designed a data lake to store data points of millions of houses in California for business development.
- Incorporated AI assistants acting as domain matter experts to offer precise and contextually relevant GIS support.
- Implemented processes for continuous updates and enhancements in a data lake using a Directed Acyclic Graph (DAG)-based system.
- Developed big data ETL pipelines to ingest versioned geographical data of California from multiple providers into a data lake. Currently, this lake consists of 28 million location rows, each with 100 feature columns.
- Designed an algorithmic approach to deduplicate locations, ingested from various location providers, and assigned a unique serializable hash ID for analysts to view a single set of unique locations across California.
- Participated in submitting various regulatory ISP requirements and compliances, saving the company millions of dollars within a constrained timeframe and objective.
- Helped the company analyze potential geographies for business expansion provisions, alongside winning grants from regulators of broadband connections.
Technologies: Data Engineering, Python, GIS, Data Pipelines, QGIS, ETL, Google Earth, Single Sign-on (SSO), Flask, FastAPI, MongoDB, Docker, Apache Solr, Unicorn, LaTeX, Full-stack Development, Microsoft Power BI, Microsoft Excel, Environmental, Social, and Governance (ESG), Large Language Models (LLMs), API Design, AWS SDK, Gmail API, Technical Leadership, User Stories, Generative Artificial Intelligence (GenAI), Monitoring, OpenTelemetry, DevOps, Prompt Engineering, Back-end Architecture, Back-end Development, Informatica, Architecture, Product Management, Jira, Product Roadmaps, Product Strategy
Senior Software Engineer
2020 - 2024
Graviton Research Capital
- Developed an in-house solution for controlling distributed jobs over 1,000 servers based on a parent-child dependency graph of 1 million nodes, bridged using 1 billion input/output files of the jobs.
- Generated prompt bot services, ensuring they can provide expert-level knowledge and assistance on individual job nodes.
- Integrated skilled AI assistants to offer precise and contextually relevant support to users.
- Contributed to the CI/CD pipelines with abstracted Jinja templates for complete prod-orchestration based on the config file.
- Scheduled remote jobs based on their parent completion checks.
- Contributed to a real-time logging, alerting, and monitoring dashboard for deployed jobs.
- Handled the real-time Sentinel stream over SSH, serving restricted access control of private servers.
- Communicated with multiple trader teams to bring underlying data into a common format for daily reconciliation. This includes constructing ETL pipelines with event triggers to calculate various post-trade metrics.
- Revolutionized traders' workflows by allowing them to focus solely on strategy implementation while my services handled all other aspects. Conducted rigorous monitoring and manual checks to ensure flawless performance and zero fault tolerance.
- Deployed a live P&L dashboard upon daily data influx with access control. Deployed an ETL pipeline, API gateway, and PyPI shell command to make this system user-interactable. This included cloud automation, log monitoring, and alerts.
Technologies: Python, Big Data, ETL, Automation, Data Engineering, Machine Learning, Data Science, Artificial Intelligence (AI), Marketing Technology (MarTech), Bash Script, C++, Apache Kafka, PySpark, Pandas, NumPy, APIs, Asyncio, CI/CD Pipelines, Data Aggregation, Data Recovery, AWS Command Line Interface (CLI), C Shell, PostgreSQL, Linux, MacOS, Telegram Bots, Python 3, SQL, Elasticsearch, REST, Web Scraping, Amazon Web Services (AWS), Amazon S3 (AWS S3), Data Reporting, Data Analytics, Data Modeling, Data Visualization, Serverless, JavaScript, GitLab, Atlassian, BigQuery, Data Warehousing, Data Warehouse Design, AWS Data Pipeline Service, Jupyter Notebook, ETL Implementation & Design, ETL Development, Data Architecture, Data Quality, Data Cleaning, Data Matching, Cloud Architecture, Data Analysis, Time Series Analysis, Forecasting, Amazon SageMaker, DataGrip, Statistical Analysis, Model Development, PyTorch, Classification Algorithms, Predictive Modeling, Statistics, REST APIs, Scikit-learn, Language Models, Dashboards, Datasets, Data Collection, Machine Learning Operations (MLOps), Data Pipelines, Financial Forecasting, Business to Business (B2B), Full-stack, Data Scraping, GIS, Geographic Information Systems, Geospatial Data, Azure, Real-time Data, Data Management, Data Governance, Architecture, MySQL, Algorithms, Pricing Models, Data-driven Marketing, API Integration, Social Media APIs, Back-end, HTML, CSS, HTML5, Decision Trees, Decision Modeling, Data-driven Decision-making, User Interface (UI), Amazon API, eCommerce APIs, Automation Scripting, Large-scale Projects, Analytical Dashboards, Software Development, GPU Computing, Data Processing, Amazon, Scraping, Amazon Marketplace, Interactive Brokers API, Multithreading, Trading Systems, Ansible, Google Maps API, Plotly, Plotly.js, GitHub, GitHub API, D3.js, Software Architecture, Google Cloud Platform (GCP), Scala, Java, Automated Data Flows, Time Series, Reinforcement Learning, Integration, Business Analysis, R, Analytics, Data Manipulation, Amazon DynamoDB, Leadership, Git, System Design, OpenAI GPT-3 API, Single Sign-on (SSO), FastAPI, RabbitMQ, Kafka Streams, Flask, MongoDB, Acceptance Testing, OpenAI, Finance, Rust, Go, Apache Solr, Unicorn, LaTeX, GraphQL, GitHub Actions, Cryptography, Databricks, Full-stack Development, Vue, Microsoft Power BI, Microsoft Excel, Graph Databases, API Design, API Development, Troubleshooting, Unix, Object-relational Mapping (ORM), AWS SDK, Gmail API, Technical Leadership, User Stories, React, High-frequency Trading (HFT), Trading, Cryptocurrency, Backtesting Trading Strategies, Monitoring, OpenTelemetry, DevOps, Prompt Engineering, Retrieval-augmented Generation (RAG), Azure Machine Learning, Azure OpenAI Service, Back-end Architecture, Back-end Development, Informatica, Jira, Product Strategy
Business Intelligence Expert
2022 - 2023
Reward Gateway
- Developed a BI dashboard on Sisense using data from a Big Data cluster served via MariaDB.
- Built a dashboard that serves real-time and historical analytics with visual graphs and interactive filters.
- Profiled older SQL queries and increased their performance by 12 times using best industry practices.
- Tracked issues via Jira, handling 221 of them: 112 older tickets and 109 new ones assigned to me.
- Served two sub-clients, Deutsche Bank and Ericsson, and was able to deliver all the requested use cases and functionalities.
Technologies: Business Intelligence (BI), Sisense, Reporting, MariaDB, Big Data, SQL, Datasets, Data Collection, Data Pipelines, Marketing Attribution, Business to Business (B2B), Marketing Mix Modeling, Full-stack, NoSQL, Real-time Data, Data Management, Data Governance, Architecture, MySQL, Algorithms, Pricing Models, Data-driven Marketing, API Integration, Social Media APIs, Back-end, AWS Lambda, HTML, CSS, HTML5, Expert Systems, ChatGPT, Amazon API, Automation Scripting, Large-scale Projects, eCommerce, Analytical Dashboards, Twilio API, Software Development, GPU Computing, Data Processing, Web Development, Amazon, Scraping, Amazon Marketplace, Google Maps API, Minimum Viable Product (MVP), Plotly, Plotly.js, GitHub, GitHub API, Software Architecture, Google Cloud Platform (GCP), Automated Data Flows, Time Series, Business Analysis, Analytics, Data Manipulation, Generative Artificial Intelligence (GenAI), Leadership, Git, System Design, OpenAI GPT-3 API, OpenAI GPT-4 API, Single Sign-on (SSO), Flask, FastAPI, MongoDB, Docker, Full-stack Development, Vue, Microsoft Power BI, Microsoft Excel, Environmental, Social, and Governance (ESG), API Design, Back-end Development, Informatica, Jira, Product Strategy
Senior Data Scientist | ML Engineer
2020 - 2023
Fello
- Developed an automated valuation model (AVM) to predict the sales price of homes in the US. Led the project since its inception, resulting in the team's expansion from one to five members over time.
- Designed, built, and implemented deep learning-based QA Chatbots and search engines for sales and marketing teams. These bots were generated using in-house data and trained LLMs from OpenAI, including GPT-3.
- Contributed to these chatbots, providing prompt responses to varied stakeholders, including procurement, refurbishing, and valuation teams. They also interpret satellite imagery for use in our AI model to support conditional price adjustments.
- Generated point-in-time sales price predictions for seven million houses in Ohio with competitive accuracy compared to market leaders like Zillow, which spent millions of dollars to achieve similar benchmarks over the past few years.
- Handled the explainability of this AI model by generating nearby similar houses with similar ranks to explain and convince end-users why their home is marked at a specific price band.
- Evaluated and integrated datasets from multiple real estate data providers, assessing them for quality and correctness, and checked our models' applicability over AWS infrastructure centered around Sagemaker.
- Generated historical housing trends based on individual geography to help users understand the market. Continued an ongoing project for market trend forecasting to help online buyers with unforeseen investment ROI.
- Built market trend insights on Amazon Quicksight and Mode.com. These trends have added to geographical segregation and restrictive access controls, and are built so that project managers or non-techy clients can iterate on their own.
- Optimized the runtime complexities of AWS QuickSight manyfold and laid the foundation for real-time, streamed insights from data. These BI charts were quite interactive and human-friendly, too.
Technologies: Python, Big Data, Artificial Intelligence (AI), ETL, PyCharm, Data Science, Google Analytics, Automation, Google Cloud, HubSpot, Pandas, NumPy, Scikit-learn, Amazon SageMaker, Data Engineering, PostgreSQL, Database Administration (DBA), Asyncio, GitLab CI/CD, AWS Command Line Interface (CLI), Valuation, MacOS, Telegram Bots, Python 3, SQL, Business Intelligence (BI), APIs, REST, Web Scraping, Amazon QuickSight, Amazon Web Services (AWS), Amazon S3 (AWS S3), Machine Learning, Redshift, Data Reporting, Data Analytics, AWS Glue, Data Modeling, Data Visualization, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), SAS Business Intelligence (BI), Real-time Business Intelligence, Node.js, Tableau, GitLab, Atlassian, Data Lakes, Data Warehousing, Data Warehouse Design, Data Lake Design, AWS Data Pipeline Service, Jupyter Notebook, ETL Implementation & Design, ETL Development, Data Architecture, Data Quality, Data Cleaning, Data Matching, Cloud Architecture, Data Analysis, Google BigQuery, Time Series Analysis, Forecasting, Amazon Machine Learning, DataGrip, Statistical Analysis, Model Development, Deep Learning, PyTorch, Classification Algorithms, Spotfire, Predictive Modeling, Statistics, REST APIs, Language Models, Dashboards, Kibana, Datasets, Data Collection, Computer Vision, R, Machine Learning Operations (MLOps), Data Pipelines, Financial Forecasting, Marketing Attribution, Business to Business (B2B), Marketing Mix Modeling, Full-stack, NoSQL, Data Scraping, Kubernetes, GIS, Geographic Information Systems, GeoPandas, Geospatial Data, Azure, Real-time Data, Data Management, Data Governance, Architecture, MySQL, Algorithms, Pricing Models, Data-driven Marketing, API Integration, Social Media APIs, Back-end, AWS Lambda, HTML, CSS, HTML5, Expert Systems, Decision Trees, Decision Modeling, Data-driven Decision-making, User Interface (UI), Amazon API, eCommerce APIs, Automation Scripting, Large-scale Projects, eCommerce, Analytical Dashboards, Twilio API, Software Development, GPU Computing, Data Processing, Amazon, Scraping, Amazon Marketplace, Multithreading, Ansible, Minimum Viable Product (MVP), Plotly, Plotly.js, GitHub, GitHub API, Software Architecture, Google Cloud Platform (GCP), Automated Data Flows, Time Series, Reinforcement Learning, Integration, Business Analysis, Algorithmic Trading, Pine Script, Analytics, Data Manipulation, Amazon DynamoDB, Terraform, Large Language Models (LLMs), Generative Artificial Intelligence (GenAI), Leadership, Git, System Design, Single Sign-on (SSO), Flask, FastAPI, MongoDB, Docker, CI/CD Pipelines, GraphQL, GitHub Actions, Cryptography, Rust, Databricks, Microsoft Power BI, Microsoft Excel, Discord, Object-relational Mapping (ORM), AWS SDK, Marketplaces, HubSpot CRM, DevOps, Back-end Development
Machine Learning Engineer
2018 - 2019
Fourkites
- Developed a Kafka-streamed, Spark ETL pipeline for big data processing in Hadoop clusters to produce AI prediction metrics over various geographies on Grafana.
- Designed a feature layer over an AWS data lake to ease infra consumption by individual ML models.
- Orchestrated automation and alerts using Airflow.
- Eased the coupling between training and production infra using TensorFlow Serving.
- Tested code in the UAT and staging environments before pushing it for production.
- Performed exploratory data analysis (EDA) for business development and insights using pandas.
- Developed microservices with API integrations for seamless SaaS operations on the ROR platform.
- Built insights dashboards on Sisense and Grafana. These dashboards are built so that project managers, non-techy clients, or clients' end users can find them easy to use. My major contribution was improving the SQL query time by 10x.
Technologies: Artificial Intelligence (AI), Automation, Big Data, ETL, Apache Airflow, Apache Kafka, PySpark, Python, Sisense, Grafana, B2C Marketing, Client Success, Hadoop, Apache Hive, Kubernetes, TensorFlow Deep Learning Library (TFLearn), Pandas, NumPy, Ruby on Rails 4, PostgreSQL, EDA, MacOS, Slack, Python 3, SQL, Elasticsearch, TensorFlow, Spark, Business Intelligence (BI), APIs, REST, Web Scraping, Amazon QuickSight, Amazon Web Services (AWS), Amazon S3 (AWS S3), Machine Learning, Redshift, Data Reporting, Data Analytics, AWS Glue, Data Modeling, Data Visualization, SAS Business Intelligence (BI), JavaScript, Tableau, Data Science, GitLab, Atlassian, BigQuery, Data Lakes, Data Warehousing, Data Warehouse Design, Data Lake Design, AWS Data Pipeline Service, Jupyter Notebook, ETL Implementation & Design, ETL Development, Data Architecture, Data Quality, Data Cleaning, Data Matching, Cloud Architecture, Data Analysis, Forecasting, Amazon Machine Learning, Statistical Analysis, Spotfire, Predictive Modeling, REST APIs, Scikit-learn, Language Models, Dashboards, Datasets, Data Collection, Machine Learning Operations (MLOps), Data Pipelines, Full-stack, Data Scraping, GIS, Geographic Information Systems, Geospatial Data, MySQL, Algorithms, API Integration, Back-end, Decision Trees, Decision Modeling, Data-driven Decision-making, Django, Automation Scripting, Large-scale Projects, Twilio API, Software Development, Data Processing, Web Development, Amazon, Scraping, Amazon Marketplace, Interactive Brokers API, Multithreading, Ruby, Plotly, Plotly.js, GitHub, GitHub API, D3.js, Software Architecture, Scala, Java, Automated Data Flows, Time Series, Reinforcement Learning, Integration, Business Analysis, Neo4j, Blockchain, Analytics, Data Manipulation, Git, CI/CD Pipelines, LaTeX, GitHub Actions, Microsoft Power BI, Microsoft Excel, DevOps
Open Source Contributor
2017 - 2019
Plone
- Developed a Plone add-on named collective.ifttt, which acts as a webhook integrated with IFTTT services to allow auto-exchange of information between platforms. If news were published on the site, it would automatically tweet about it.
- Awarded #1 Plone add-on of 2018 in the annual conference of Plone held in Tokyo for the collective.ifttt add-on.
- Developed another Plone add-on named plone.importexport, which deserializes Zope data into human-readable FileSystem format to assist non-techy users with CRUD operations on data like import and export.
- Oversaw upgrade of the plone.importexport add-on in the following years to serve it as core servings of Plone, a Python CMS platform.
Technologies: Python, Webhooks, Acceptance Testing, CI/CD Pipelines, Sphinx Documentation Generator, Plone, IFTTT, Jenkins, PyPI, PEP 8, Python 3, APIs, REST, GitLab, Full-stack, Algorithms, Django, Scraping, Software Architecture, Neo4j, Git, Flask, LaTeX
Software Developer
2016 - 2018
FairShuffle
- Designed a client-side graphics rendering framework for online card games with real-time updates.
- Performed Async rendering of various layers without any observable delay. The primary challenges were the ability to adhere to multiple themes based on the configuration provided by Adobe PhotoShop.
- Multiple wow effects were also highlighted in this game to attract user attention.
Technologies: Fabric, React, Node.js, MongoDB, Serverless, JavaScript, GitLab, Mobile Games, Algorithms, Django, Web Development, Amazon Marketplace, Ruby, Minimum Viable Product (MVP), Software Architecture, Neo4j, Blockchain, Flask, Cryptography
Project History
Sale Price Predictions & AI Copilots for the US Housing Market
https://hifello.com/At Fello, I architected an AI-powered property intelligence platform for the Ohio housing market, combining machine learning, large-scale data engineering, and generative AI to deliver enterprise-grade valuation and investment insights. The platform processed over 10 million residential property records, generated real-time property valuations, identified comparable homes, and powered AI copilots for investors and homebuyers. Our valuation models achieved accuracy competitive with Zillow's published Zestimate benchmarks for listed homes, demonstrating that a lean startup team could build enterprise-grade AI solutions.
Key Contributions:
• Architected an AI valuation platform processing 10+ million Ohio property records.
• Engineered ETL pipelines integrating MLS, assessor, tax, demographic, and third-party datasets.
• Developed ML models with competitive accuracy against Zillow's published benchmarks.
• Built AI-powered comparable property search and historical market analytics.
• Developed LLM copilots for natural-language property search and investment insights.
• Optimized cloud-native APIs for low-latency, production-scale inference.
Key Contributions:
• Architected an AI valuation platform processing 10+ million Ohio property records.
• Engineered ETL pipelines integrating MLS, assessor, tax, demographic, and third-party datasets.
• Developed ML models with competitive accuracy against Zillow's published benchmarks.
• Built AI-powered comparable property search and historical market analytics.
• Developed LLM copilots for natural-language property search and investment insights.
• Optimized cloud-native APIs for low-latency, production-scale inference.
PnL Dashboard for Algorithmic Trading Firm
I engineered a high-performance real-time PnL and risk analytics dashboard for an algorithmic trading firm, delivering actionable insights across portfolios, strategies, executions, and historical performance. I designed complex aggregation pipelines and multidimensional filtering to enable sub-second analytics over large financial datasets.
I also built a Kafka-based streaming platform that processes 10+ million market events and trade updates daily, alongside scalable Python ETL pipelines that extract historical data from AWS S3 into optimized analytical stores for reporting and trend analysis.
Additionally, I developed REST APIs and CLI integrations with proprietary trading systems, enabling automated strategy monitoring, portfolio analysis, and operational workflows. I also optimized back-end queries and data processing, reducing report generation time by 65% while maintaining 99.9% production availability.
Key achievements:
• 10+ million events per day
• Sub-second real-time analytics
• 65% faster reporting
• 99.9% uptime
It was built with Python, Apache Kafka, Grafana, PostgreSQL, AWS, ETL, and REST APIs.
I also built a Kafka-based streaming platform that processes 10+ million market events and trade updates daily, alongside scalable Python ETL pipelines that extract historical data from AWS S3 into optimized analytical stores for reporting and trend analysis.
Additionally, I developed REST APIs and CLI integrations with proprietary trading systems, enabling automated strategy monitoring, portfolio analysis, and operational workflows. I also optimized back-end queries and data processing, reducing report generation time by 65% while maintaining 99.9% production availability.
Key achievements:
• 10+ million events per day
• Sub-second real-time analytics
• 65% faster reporting
• 99.9% uptime
It was built with Python, Apache Kafka, Grafana, PostgreSQL, AWS, ETL, and REST APIs.
Feature Layer over Data Lake
https://www.fourkites.com/Individual ML models have feature transformation requirements that incur high server infrastructure costs.
I designed a Feature layer over the firm's data lake, where the requirements of all ML models are examined to serve a common Feature layer, thereby reducing the firm's infrastructure consumption by 14x.
The challenge was to bring harmony among features, ensuring that there was no hampering of any running ML service in production.
I designed a Feature layer over the firm's data lake, where the requirements of all ML models are examined to serve a common Feature layer, thereby reducing the firm's infrastructure consumption by 14x.
The challenge was to bring harmony among features, ensuring that there was no hampering of any running ML service in production.
Process Controller for Distributed Jobs over In-house Data Center
https://ripple.gravitontrading.com/I developed an in-house solution for controlling distributed jobs over 1,000 servers based on a parent-child dependency graph of one million nodes, bridged using one billion input/output files of the jobs.
Additional functionalities of this project were—and every single one of these services was designed and developed from scratch using various state-of-the-art technologies, such as Python, Bash, psql, and C++:
• CI/CD pipelines with abstracted Jinja templates for complete prod-orchestration based on the config file.
• A real-time logging, alerting, and monitoring dashboard for deployed jobs.
• Scheduling remote jobs based on their parent completion checks.
• A real-time Sentinel stream over SSH serving restricted access control of private servers.
Additional functionalities of this project were—and every single one of these services was designed and developed from scratch using various state-of-the-art technologies, such as Python, Bash, psql, and C++:
• CI/CD pipelines with abstracted Jinja templates for complete prod-orchestration based on the config file.
• A real-time logging, alerting, and monitoring dashboard for deployed jobs.
• Scheduling remote jobs based on their parent completion checks.
• A real-time Sentinel stream over SSH serving restricted access control of private servers.
Education
2014 - 2018
Bachelor's Degree in Computer Science
International Institute of Information Technology (IIIT-H) - Hyderabad, India
Skills
Libraries/APIs
Pandas, REST APIs, Interactive Brokers API, Plotly.js, GitHub API, API Development, Scikit-learn, React, Node.js, PyTorch, Social Media APIs, Amazon API, Twilio API, Google Maps API, D3.js, Vue, Gmail API, PySpark, TensorFlow Deep Learning Library (TFLearn), NumPy, Asyncio, Fabric, TensorFlow, Luigi
Tools
PyCharm, Amazon SageMaker, GitLab, Plotly, GitHub, Git, LaTeX, Microsoft Power BI, Microsoft Excel, Jira, Claude, Slack, Grafana, Amazon QuickSight, AWS Glue, Tableau, Atlassian, BigQuery, DataGrip, Spotfire, GIS, ChatGPT, Ansible, Terraform, RabbitMQ, Apache Solr, AWS SDK, Azure Machine Learning, Azure OpenAI Service, Google Analytics, Apache Airflow, Sisense, AWS Command Line Interface (CLI), GitLab CI/CD, Jenkins, PyPI, SAS Business Intelligence (BI), Kibana, Kafka Streams
Languages
Python, Bash Script, Python 3, SQL, JavaScript, R, HTML, CSS, HTML5, Ruby, Scala, Pine Script, Rust, Go, Unicorn, GraphQL, Snowflake, C++, C Shell, Java, Solidity, SPARQL
Frameworks
Flask, Spark, Django, Apache Spark, Hadoop, Ruby on Rails 4, Sphinx Documentation Generator, Plone, Jinja
Paradigms
ETL, Automation, REST, ETL Implementation & Design, Back-end Architecture, Business Intelligence (BI), Object-relational Mapping (ORM), DevOps, Acceptance Testing, Agile Software Development
Platforms
MacOS, Linux, Apache Kafka, Amazon Web Services (AWS), Jupyter Notebook, Docker, Unix, Kubernetes, HubSpot, Azure, AWS Lambda, Amazon, Google Cloud Platform (GCP), Databricks, IFTTT, Blockchain, Jet Admin, Twilio, The Open Network (TON), Oracle Integration Cloud (OIC)
Storage
PostgreSQL, MongoDB, Amazon S3 (AWS S3), Data Pipelines, MySQL, Elasticsearch, Redshift, Data Lakes, Data Lake Design, AWS Data Pipeline Service, NoSQL, Amazon DynamoDB, Graph Databases, Apache Hive, Google Cloud, Database Administration (DBA), MariaDB, Neo4j
Industry Expertise
High-frequency Trading (HFT), Trading Systems
Other
Telegram Bots, Big Data, Data Engineering, Data Science, APIs, CI/CD Pipelines, Web Scraping, Data Reporting, Data Analytics, ETL Development, Data Architecture, Data Analysis, Datasets, Data Collection, Machine Learning Operations (MLOps), Architecture, API Integration, Automation Scripting, Large-scale Projects, Analytical Dashboards, Software Development, Data Processing, Multithreading, Software Architecture, Automated Data Flows, Business Analysis, Analytics, Data Manipulation, FastAPI, Finance, API Design, Troubleshooting, Trading, Back-end Development, Informatica, Product Management, Product Roadmaps, Product Strategy, Artificial Intelligence (AI), Machine Learning, Data Modeling, Data Visualization, Natural Language Processing (NLP), Serverless, Data Warehousing, Data Warehouse Design, Data Quality, Data Cleaning, Data Matching, Cloud Architecture, Time Series Analysis, Forecasting, Amazon Machine Learning, Statistical Analysis, Model Development, Classification Algorithms, Mobile Games, Predictive Modeling, Statistics, Dashboards, Computer Vision, Financial Forecasting, Marketing Attribution, Business to Business (B2B), Full-stack, Data Scraping, Geographic Information Systems, GeoPandas, Geospatial Data, Real-time Data, Data Management, Data Governance, Algorithms, Pricing Models, Data-driven Marketing, Back-end, Expert Systems, Decision Trees, Decision Modeling, Data-driven Decision-making, Chatbots, User Interface (UI), eCommerce APIs, eCommerce, GPU Computing, Web Development, Scraping, Amazon Marketplace, Minimum Viable Product (MVP), Time Series, Reinforcement Learning, Integration, Algorithmic Trading, Large Language Models (LLMs), Generative Artificial Intelligence (GenAI), Leadership, System Design, Single Sign-on (SSO), OpenAI, GitHub Actions, Cryptography, Full-stack Development, Environmental, Social, and Governance (ESG), Semantic Search, Discord, Technical Leadership, User Stories, Marketplaces, HubSpot CRM, Cryptocurrency, Backtesting Trading Strategies, Monitoring, OpenTelemetry, Azure Databricks, Azure Data Factory (ADF), Prompt Engineering, Retrieval-augmented Generation (RAG), Marketing Technology (MarTech), B2C Marketing, Client Success, EDA, Data Aggregation, Data Recovery, Valuation, Webhooks, PEP 8, Cloud Data Fusion, Real-time Business Intelligence, Google BigQuery, Deep Learning, Language Models, Reporting, Marketing Mix Modeling, Generative Pre-trained Transformers (GPT), OpenAI GPT-3 API, OpenAI GPT-4 API, QGIS, Google Earth, Graphs, Amazon Bedrock AgentCore, Streaming, AI Agents, AgentGPT, Ontologies, Semantics
Collaboration That Works
How to Work with Toptal
Toptal matches you directly with global industry experts from our network in hours—not weeks or months.
1
Share your needs
Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2
Choose your talent
Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3
Start your risk-free talent trial
Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.
Top talent is in high demand.
Start hiring