Snigdha is available for hire
Hire SnigdhaSnigdha Sharma
Verified Expert in Engineering
Artificial Intelligence Developer
Bengaluru, Karnataka, India
Toptal member since August 23, 2023
Bio
Snigdha is an experienced AI engineer specializing in software development, NLP, computer vision, AI, Data Science, and AWS technologies. She's worked with companies of many shapes and sizes, from a month-old startup still iterating on its ideas to a large bank like JPMorgan Chase and one of the fastest growing names in HR tech—Eightfold.ai. Snigdha has gained insightful experience in several industries, including pharmaceuticals, media and entertainment, national security, fintech, and HR tech.
Portfolio
Mindchords.ai
Amazon Web Services (AWS), Python 3, Generative Artificial Intelligence (GenAI)...
Eightfold AI
Python 3, OpenAI GPT-4 API, XGBoost, Spark, Software Development...
JPMorgan Chase
Amazon Web Services (AWS), Natural Language Processing (NLP), PyTorch...
Experience
- Natural Language Processing (NLP) - 8 years
- Machine Learning - 8 years
- Python 3 - 7 years
- Git - 7 years
- Software Development - 6 years
- Amazon Web Services (AWS) - 6 years
- Neo4j - 4 years
- Generative Artificial Intelligence (GenAI) - 4 years
Preferred Environment
Linux, Git, Windows, Python
The most amazing...
...use case I have helped a business deliver is automating 60% of the load off their customer support teams, allowing them focus on more pressing tickets.
Work Experience
AI Consultant | Fractional AI Head
2024 - PRESENT
Mindchords.ai
- Defined and executed an AI strategy and roadmap for an HR tech product startup, and helped hire the right team for execution. Worked on use case discovery and feasibility analysis.
- Delivered end-to-end Voice AI agents to automate around 60% load on customer support teams for an insurance company. Developed custom workflows that integrate with customer data securely, and implemented custom checks to reduce speech-to-text issues.
- Built AI agents that help automate client intake processes for legal firms. The voice agent handles edge cases, response ambiguity, data validation, and non-deterministic conversation flows (data collected depends on the client's initial answers).
- Developed and deployed end-to-end AI agents (both text and voice-based) to automate around 60% load from customer support teams for an insurance company.
- Used retrieval-augmented generation (RAG) to develop a customer support bot that answers user queries, taking help from massive product documentation, with close to 0 hallucinations. Implemented testing pipelines to streamline QA for the chatbot.
- Developed a RAG pipeline with vector store in Supabase, a LlamaIndex for an agritech compliance platform.
- Implemented several use cases in production using prompt engineering and fine-tuning LLMs, including summarization, Q&A, text evaluation, information extraction, code generation, data analysis, reasoning, personalization, and many more.
- Developed a data analyst tool using generative AI that can talk to data while ensuring the explainability of the results and minimum hallucination.
- Reformatted unstructured documents to create clean, unambiguous, nonrepetitive, and well-structured documents.
- Improved prompts for several clients using best practices and extensive insights gained by working on several prompting frameworks.
Technologies: Amazon Web Services (AWS), Python 3, Generative Artificial Intelligence (GenAI), OpenAI, Machine Learning, Embeddings from Language Models (ELMo), Retrieval-augmented Generation (RAG), Large Language Models (LLMs), AI Model Training, Prompt Engineering, Pinecone, Product Design, Product Ownership, Scalable Vector Databases, Multimodal Models, LangChain, Machine Learning Operations (MLOps), Distributed Systems, Pandas, SQL, English, Gemini, AI Agents, Llama 2, Vector Databases, Data Modeling, Modeling, Data Annotation, Text Analytics, Speech Analytics, Speech-to-Text (STT), Voice Analysis, AI Chatbots, AI Prompts, Text to Image, DALL-E, Data Architecture, Claude, Data Aggregation, Data Scraping, Vercel, Deployment, Flask, FastAPI, MongoDB, PostgreSQL, Streamlit, Web Scraping, Large Language Model Operations (LLMOps), n8n, Graph Databases, Knowledge Graphs, LangGraph, Agentic AI, Agentic Frameworks, Amazon Lex, Dialogflow, Rasa NLU, Twilio, Rasa.ai, Architecture, AI Automation, Model Context Protocol (MCP), Supabase, LlamaIndex, Amazon Bedrock AgentCore, API Integration, Third-party Integration, TypeScript, React, Predictive Analytics, Text Classification, Minimum Viable Product (MVP), Legal Technology (Legaltech), Classification, ElevenLabs Solutions, AI Voice Agents, API Design, Twilio API, WebSockets, Serverless Architecture, Microservices, Embedding Models, Fine-tuning, Google Cloud Platform (GCP), Real-time Data, Agentic AI Systems, UiPath, Pgvector, Claude Code, Light LLMs, AI Agent Orchestration, Workflow Automation, Agentic RAG Systems, LangSmith, Semantic Search, Conversational AI, CRM Implementation (Salesforce), Real-time Systems, Multi-agent Systems, RAG Architecture, REST APIs, Workflow Automation & System Integration, Decision Modeling, AI Design, Agentic Workflow Design, Automatic Speech Recognition (ASR), Anthropic, Model Evaluation, Agentic Coding, Audio Processing, API Gateways, Telemetry, Cursor AI
Senior ML Engineer
2021 - 2024
Eightfold AI
- Led a generative AI team to develop features that can transform how users interact with our product.
- Implemented many use cases with prompt engineering and generative AI. Thoroughly tested these prompts on production test datasets and achieved significant improvements as compared to generic prompt libraries out there.
- Launched the product's first AI text-generation use case using OpenAI models and prompt engineering. Also implemented streaming to reduce effective latencies and caching to reduce OpenAI request costs.
- Won the "Most Innovative Hack" award among 80 teams and led a team of two to re-imagine dashboards showing hiring analytics. I talked to stakeholders, understood pain points, and demoed an ML-based solution that has since been launched for our users.
- Resolved customer issues, critical technical bugs, and on-call issues in both resume parsing and matching and ranking modules in specific and integration problems with other services in the product.
- Implemented human-annotated datasets to track ML model launches, reducing harmful recommendations by approximately 2% and improving feature engineering.
- Guided initiatives on improving job recommendations for internal employees and skill extraction improvements for better skill-based hiring.
- Owned and delivered personal contact details and education extraction modules using entity extraction techniques, achieving a better AUC than 3rd-party services. I also set up pipelines to track metrics and latencies in production.
- Participated in the ML hiring pipeline and interviewed candidates, onboarding and mentoring interns and new graduates amongst the top talent pool.
- Researched and presented learning sessions to the team to understand new ML solutions to product problems.
Technologies: Python 3, OpenAI GPT-4 API, XGBoost, Spark, Software Development, Machine Learning, Software Design, Linux, Git, Entrepreneurship, Scikit-learn, SpaCy, Natural Language Processing (NLP), Amazon Web Services (AWS), Named-entity Recognition (NER), Indexing, Tf-idf, DistilBERT, CI/CD Pipelines, Generative Pre-trained Transformers (GPT), OpenAI GPT-3 API, Team Leadership, Planning, Stakeholder Management, ChatGPT, Artificial Intelligence (AI), Python, Generative Pre-trained Transformer 3 (GPT-3), Audio, OpenAI API, Data Analysis, Feature Engineering, MacOS, Flask, Deep Learning, Computer Science, Regression, Hypothesis Testing, Data Science, Data Analytics, Data Visualization, Statistics, Chatbots, Chatbot Conversation Design, Data Engineering, ETL, Cloud Architecture, Recommendation Systems, Artificial General Intelligence (AGI), APIs, Technical Leadership, Leadership, Mentorship & Coaching, Large Language Models (LLMs), OpenAI, Software Architecture, Cloud, Algorithms, Models, Generative Artificial Intelligence (GenAI), LoRa, Hugging Face, Retrieval-augmented Generation (RAG), BERT, AI Model Training, Prompt Engineering, Pinecone, Product Design, Product Ownership, Scalable Vector Databases, LangChain, Machine Learning Operations (MLOps), Distributed Systems, Pandas, Docker, SQL, English, Gemini, AI Agents, Vector Databases, Data Modeling, Modeling, Data Annotation, Text Analytics, Speech Analytics, Speech-to-Text (STT), Voice Analysis, AI Chatbots, AI Prompts, Data Architecture, Data Aggregation, Data Scraping, Deployment, Vercel, FastAPI, PostgreSQL, MongoDB, Web Scraping, Large Language Model Operations (LLMOps), Architecture, AI Automation, API Integration, Third-party Integration, TypeScript, React, Predictive Analytics, Text Classification, Minimum Viable Product (MVP), Classification, API Design, WebSockets, Serverless Architecture, Microservices, Embedding Models, Fine-tuning, Agentic AI Systems, UiPath, Pgvector, AI Agent Orchestration, Agentic RAG Systems, Semantic Search, Conversational AI, Real-time Systems, Multi-agent Systems, RAG Architecture, REST APIs, Workflow Automation & System Integration, Decision Modeling, AI Design, Agentic Workflow Design, Anthropic, Model Evaluation, API Gateways
ML Engineer | Data Scientist
2020 - 2021
JPMorgan Chase
- Envisioned and developed a product to derive insights from earning call transcripts for around 500 public companies using prompt engineering. I extensively researched 3rd-party tools to create product features and architecture design documents.
- Introduced a pipeline for information retrieval from PDF documents using GPT-2, DistilBERT, and text similarity using BERT embeddings. Analysts can use the system to create reports, saving almost 70% of manual reading effort.
- Leveraged a graph variational auto-encoder and Neo4j to calculate node similarity in a knowledge network graph of potential clients.
- Launched a NER API using Python, TensorFlow, and MLflow to automatically annotate documents using some pre-defined labels and saving hours of annotation effort.
- Deployed multiple apps using CI/CD in Jenkins on the internal Kubernetes platform. I devised interactive dashboards to analyze client emails in shared mailboxes and present insights from earning call transcripts for around 500 public companies.
- Held the top position with teams in internal hackathons.
Technologies: Amazon Web Services (AWS), Natural Language Processing (NLP), PyTorch, TensorFlow, SpaCy, Named-entity Recognition (NER), Sentiment Analysis, Indexing, Tf-idf, DistilBERT, MLflow, Graph Neural Networks (GNNs), CI/CD Pipelines, Kubernetes, Software Development, Machine Learning, Software Design, Linux, Git, Windows, Python 3, XGBoost, Scikit-learn, Generative Pre-trained Transformers (GPT), Planning, Stakeholder Management, Artificial Intelligence (AI), Python, Generative Pre-trained Transformer 3 (GPT-3), OpenAI API, Data Analysis, Feature Engineering, Flask, Deep Learning, Computer Science, Regression, Hypothesis Testing, Data Science, Data Analytics, Data Visualization, Statistics, Data Engineering, ETL, Cloud Architecture, Recommendation Systems, Artificial General Intelligence (AGI), TensorFlow Deep Learning Library (TFLearn), APIs, Technical Leadership, Leadership, Finance, Mentorship & Coaching, Large Language Models (LLMs), OpenAI, Software Architecture, Cloud, Algorithms, Models, Generative Artificial Intelligence (GenAI), Hugging Face, Retrieval-augmented Generation (RAG), BERT, Text-to-Speech (TTS), AI Model Training, Prompt Engineering, Product Design, Product Ownership, Machine Learning Operations (MLOps), Distributed Systems, Pandas, AWS Lambda, Docker, SQL, English, Vector Databases, Data Modeling, Modeling, Data Annotation, Text Analytics, Speech Analytics, Speech-to-Text (STT), Voice Analysis, AI Prompts, Data Architecture, Data Aggregation, Data Scraping, Deployment, Vercel, FastAPI, PostgreSQL, MongoDB, Web Scraping, Graph Databases, Knowledge Graphs, Architecture, AI Automation, API Integration, Third-party Integration, Text Classification, Minimum Viable Product (MVP), Classification, API Design, WebSockets, Serverless Architecture, Embedding Models, Fine-tuning, Workflow Automation, Semantic Search, Real-time Systems, REST APIs, Workflow Automation & System Integration, AI Design, Model Evaluation, API Gateways
Senior Product Engineer
2019 - 2020
Myelin Foundry
- Researched current architectures and performed E2E ML modeling to train content-aware neural network-based super-resolution models that can be applied to videos in real time to improve the OTT media user's experience and reduce bandwidth usage.
- Released a fully functional POC demonstrating the above capability for desktop and Android, which earned the project its first client, a top video streaming company.
- Delivered a POC to de-noise SAR satellite images using U-Net-like architecture.
Technologies: Computer Vision, Natural Language Processing (NLP), Python 3, TensorFlow, Software Development, Machine Learning, Software Design, Linux, Git, Entrepreneurship, Azure, PyTorch, Planning, Stakeholder Management, Artificial Intelligence (AI), Python, Data Analysis, Feature Engineering, Flask, Convolutional Neural Networks (CNNs), Deep Learning, Computer Science, Data Visualization, Image Processing, Artificial General Intelligence (AGI), Generative Adversarial Networks (GANs), APIs, Technical Leadership, Leadership, Mentorship & Coaching, Large Language Models (LLMs), Software Architecture, Cloud, Algorithms, Models, Generative Artificial Intelligence (GenAI), BERT, Text-to-Speech (TTS), AI Model Training, Product Design, Product Ownership, Machine Learning Operations (MLOps), Pandas, Docker, English, Data Modeling, Modeling, Data Annotation, Data Architecture, Data Aggregation, Data Scraping, Deployment, Vercel, Amazon Web Services (AWS), FastAPI, MongoDB, PostgreSQL, Web Scraping, Architecture, API Integration, Third-party Integration, Minimum Viable Product (MVP), Classification, API Design, Embedding Models, Fine-tuning, Real-time Data, Real-time Systems, REST APIs, AI Design, Model Evaluation, API Gateways
Associate Software Developer – Research and Development
2017 - 2019
Axtria
- Prepared a business case study for sales prediction based on investments in different promotional channels and lagging sales.
- Trained a custom neural network in TensorFlow and visualized results in TensorBoard with a 71% reduction in error.
- Developed a modular, meta-data driven, multi-threaded, scalable Flask app for business rule execution to automate sales crediting and incentive compensation processes, integrating Salesforce and Amazon S3, Redshift, and Athena.
- Designed a wrapper web app for Amazon Polly, introducing additional features such as parsing text from single or multiple files to convert it into downloadable MP3 speech files for internal usage.
Technologies: Amazon Web Services (AWS), Python 3, Redshift, PostgreSQL, Salesforce, Software Development, Machine Learning, Software Design, Git, Windows, Entrepreneurship, XGBoost, Scikit-learn, TensorFlow, Planning, Stakeholder Management, Artificial Intelligence (AI), Python, Audio, Data Analysis, Feature Engineering, Flask, Deep Learning, Computer Science, Regression, Data Science, Data Analytics, Data Visualization, Statistics, Data Engineering, ETL, Cloud Architecture, TensorFlow Deep Learning Library (TFLearn), APIs, Mentorship & Coaching, Software Architecture, Cloud, Algorithms, Models, Text-to-Speech (TTS), AI Model Training, Pandas, AWS Lambda, SQL, English, Data Modeling, Modeling, Data Architecture, Data Aggregation, Data Scraping, Vercel, Deployment, FastAPI, MongoDB, Web Scraping, Architecture, AI Automation, API Integration, Third-party Integration, Predictive Analytics, Minimum Viable Product (MVP), Classification, API Design, WebSockets, Serverless Architecture, Microservices, Embedding Models, Fine-tuning, Real-time Data, Real-time Systems, REST APIs, Model Evaluation, API Gateways
Experience
GenAI Copilot Suite for HR-tech Product
This project involved leading a team of four members to power an HR-tech product with generative artificial intelligence (GenAI) capabilities and make recruiting easier for our enterprise customers.
I developed the back-end software design, engineered LLM prompts, discussed improvements and feasibility with our UX design team, got my team unblocked on development issues (both back-end and front-end development), mentored interns to develop utility frameworks and modules, conducted PR reviews, discussed features to be developed with stakeholders.
The developed functionalities include:
• Creating text from scratch for custom use cases.
• Inferring user intent and refining existing templates for personalization and customization in just a few seconds.
• Summarizing information required to make hiring decisions all in one place.
• Making inferences and answering questions from long text.
All these capabilities are offered while ensuring the entire system is fit for enterprise use cases, removing confidential information before sending it to LLMs and maintaining the professionalism and accuracy of the outputs generated to be scaled for business use cases.
I developed the back-end software design, engineered LLM prompts, discussed improvements and feasibility with our UX design team, got my team unblocked on development issues (both back-end and front-end development), mentored interns to develop utility frameworks and modules, conducted PR reviews, discussed features to be developed with stakeholders.
The developed functionalities include:
• Creating text from scratch for custom use cases.
• Inferring user intent and refining existing templates for personalization and customization in just a few seconds.
• Summarizing information required to make hiring decisions all in one place.
• Making inferences and answering questions from long text.
All these capabilities are offered while ensuring the entire system is fit for enterprise use cases, removing confidential information before sending it to LLMs and maintaining the professionalism and accuracy of the outputs generated to be scaled for business use cases.
GenAI Conversational Agents for Several Industries
I developed end-to-end solutions using AI voice agents for the following use cases:
• Assist legal firms in handling their client communication processes for a given legal case.
• Automate around 60% load from customer support teams for an insurance company.
• Improve and scale marketing and lead conversion efforts for an edtech company.
Each one of these projects was a deeply customized end-to-end solution that included:
• Streamlining of chaotic processes to create clear, modular processes that can be performed with better accuracy and consistency using AI.
• Developing fallback options that involve humans when the task is out of scope for AI, without adversely affecting user experience.
• Workflow integrations with the existing tech stack of the company, also keeping in mind the operational budgets.
• Developing supporting tools to assist the agents in doing meaningful tasks more reliably, that go much beyond information-providing chatbots.
• Thorough testing for malicious users and for the expected edge case scenarios.
• Keeping in mind the brand voice of the company and the emotional intelligence necessary for critical customer-facing use cases.
• Assist legal firms in handling their client communication processes for a given legal case.
• Automate around 60% load from customer support teams for an insurance company.
• Improve and scale marketing and lead conversion efforts for an edtech company.
Each one of these projects was a deeply customized end-to-end solution that included:
• Streamlining of chaotic processes to create clear, modular processes that can be performed with better accuracy and consistency using AI.
• Developing fallback options that involve humans when the task is out of scope for AI, without adversely affecting user experience.
• Workflow integrations with the existing tech stack of the company, also keeping in mind the operational budgets.
• Developing supporting tools to assist the agents in doing meaningful tasks more reliably, that go much beyond information-providing chatbots.
• Thorough testing for malicious users and for the expected edge case scenarios.
• Keeping in mind the brand voice of the company and the emotional intelligence necessary for critical customer-facing use cases.
Industry Insights Using Earning Call Transcripts
An in-house product that I envisioned and developed using natural language processing algorithms, where the product aims to derive insights from earning call transcripts for around 500 public companies.
I worked independently on this project, communicating with the senior leadership at JPMC. Also, I conducted extensive research on the 3rd-party tools available to create Product Feature Documents and Architecture Design documents as future work to convert the above project into a competing product at scale.
I worked independently on this project, communicating with the senior leadership at JPMC. Also, I conducted extensive research on the 3rd-party tools available to create Product Feature Documents and Architecture Design documents as future work to convert the above project into a competing product at scale.
Named Entity Recognition
Independently launched a named entity recognition (NER) API using Python, TensorFlow, and MLFlow. The API:
• Automatically annotates documents by extracting entities from a database, saving approximately 500 hours of manual annotation work.
• Trains and fine-tunes NER models using Bi-LSTM CNN.
• Stores, manages, and facilitates model version comparisons.
This tool was applied to train models for extracting product opportunities from meeting notes, call transcripts, and automatically tagging client emails for automatic ticket creation.
• Automatically annotates documents by extracting entities from a database, saving approximately 500 hours of manual annotation work.
• Trains and fine-tunes NER models using Bi-LSTM CNN.
• Stores, manages, and facilitates model version comparisons.
This tool was applied to train models for extracting product opportunities from meeting notes, call transcripts, and automatically tagging client emails for automatic ticket creation.
Fovea | Video-resolution Enhancement Tool
This project involved creating an Android app—later converted into a plugin for some existing streaming apps—that transforms a low-resolution video to a higher resolution in real time. I mainly leveraged CNN-based neural networks run on Snapdragon.
I was a senior developer on a team project with 3-4 members, focusing on researching various CNN-based super-resolution architectures. We trained a CNN-based model best fit for our use case, maximizing quality while keeping the inference time low enough to allow real time video super resolution at 30fps. I integrated this model with a video and image library. Also, I integrated this in a dummy Android app to demonstrate the feasibility/quality of this model when run on Android devices. Running this on Android devices posed some challenges wherein we had to utilize a DSP runtime in Snapdragon processors, for which our team worked with some industry experts.
Additionally, I conducted research on speeding up the process by leveraging knowledge of video encoding, including i/p/b frames.
I was a senior developer on a team project with 3-4 members, focusing on researching various CNN-based super-resolution architectures. We trained a CNN-based model best fit for our use case, maximizing quality while keeping the inference time low enough to allow real time video super resolution at 30fps. I integrated this model with a video and image library. Also, I integrated this in a dummy Android app to demonstrate the feasibility/quality of this model when run on Android devices. Running this on Android devices posed some challenges wherein we had to utilize a DSP runtime in Snapdragon processors, for which our team worked with some industry experts.
Additionally, I conducted research on speeding up the process by leveraging knowledge of video encoding, including i/p/b frames.
Matching Candidates with Positions
I played a key role in a project that ranks open positions for candidates and vice versa to identify the best matches. My responsibilities included substantial enhancements to the feature engineering pipelines powering our ML models.
During this project, I:
• Spearheaded skill extraction improvements to facilitate more effective skill-based hiring.
• Led efforts to enhance job recommendations for internal employees.
• Launched human-annotated datasets, specifically tailored for internal job postings, ensuring they accurately represent real-world data. Also, I developed an objective evaluation system that the annotation team used to create a high-quality evaluation dataset. Additionally, I created a user-friendly Flask service to streamline the annotation process.
• Successfully reduced bad job recommendations for internal employees by approximately 2% through feature engineering improvements.
During this project, I:
• Spearheaded skill extraction improvements to facilitate more effective skill-based hiring.
• Led efforts to enhance job recommendations for internal employees.
• Launched human-annotated datasets, specifically tailored for internal job postings, ensuring they accurately represent real-world data. Also, I developed an objective evaluation system that the annotation team used to create a high-quality evaluation dataset. Additionally, I created a user-friendly Flask service to streamline the annotation process.
• Successfully reduced bad job recommendations for internal employees by approximately 2% through feature engineering improvements.
Education
2013 - 2017
Bachelor's Degree in Computer Science
National Institute of Technology, Delhi - New Delhi, India
Certifications
DECEMBER 2023 - PRESENT
Brand and Product Management
IE Business School
DECEMBER 2022 - PRESENT
Entrepreneurship Specialization
Wharton University of Pennsylvania | via Coursera
MAY 2021 - PRESENT
Practical Reinforcement Learning
Higher School of Economics
JANUARY 2021 - PRESENT
Bayesian Methods for Machine Learning
Higher School of Economics
DECEMBER 2020 - PRESENT
Program on Business Analytics and Machine Learning for Asset and Wealth Management
Indian Institute of Management Bangalore
DECEMBER 2020 - PRESENT
How to Win a Data Science Competition
Higher School of Economics
MAY 2020 - PRESENT
Introduction to Deep Learning
Higher School of Economics
DECEMBER 2019 - PRESENT
General Course on Intellectual Property
WIPO Academy
Skills
Libraries/APIs
XGBoost, Scikit-learn, SpaCy, PyTorch, TensorFlow, OpenAI API, Pandas, Rasa NLU, Twilio API, REST APIs, React, TensorFlow Deep Learning Library (TFLearn), Claude API
Tools
Git, Named-entity Recognition (NER), ChatGPT, AI Prompts, Claude, n8n, Amazon Lex, Dialogflow, Rasa.ai, Claude Code
Languages
Python 3, Python, SQL, TypeScript, R
Frameworks
Flask, Streamlit, LangGraph, Agentic Frameworks, LlamaIndex, Spark
Paradigms
ETL, Model Context Protocol (MCP), Serverless Architecture, Microservices, Real-time Systems
Platforms
Linux, Windows, Amazon Web Services (AWS), MacOS, AWS Lambda, Docker, Vercel, Twilio, LangSmith, Azure, Google Cloud Platform (GCP), Kubernetes, Salesforce
Storage
MongoDB, Neo4j, Graph Databases, Redshift, PostgreSQL
Other
Software Development, Machine Learning, Software Design, OpenAI GPT-4 API, Natural Language Processing (NLP), Indexing, DistilBERT, MLflow, OpenAI GPT-3 API, Generative Pre-trained Transformers (GPT), Planning, Stakeholder Management, Artificial Intelligence (AI), Generative Pre-trained Transformer 3 (GPT-3), Data Analysis, Feature Engineering, Convolutional Neural Networks (CNNs), Deep Learning, Computer Science, Regression, Hypothesis Testing, Data Science, Data Analytics, Data Visualization, Statistics, Chatbots, Chatbot Conversation Design, Image Processing, Data Engineering, Cloud Architecture, Recommendation Systems, Artificial General Intelligence (AGI), APIs, Technical Leadership, Leadership, Mentorship & Coaching, Large Language Models (LLMs), OpenAI, Software Architecture, Cloud, Algorithms, Models, Generative Artificial Intelligence (GenAI), LoRa, Hugging Face, Retrieval-augmented Generation (RAG), BERT, Text-to-Speech (TTS), Prompt Engineering, AI Model Training, Product Design, Product Ownership, Multimodal Models, LangChain, Machine Learning Operations (MLOps), English, Gemini, AI Agents, Llama 2, Vector Databases, Data Modeling, Modeling, Data Annotation, Text Analytics, Speech Analytics, Speech-to-Text (STT), Voice Analysis, AI Chatbots, Text to Image, DALL-E, Data Architecture, Data Aggregation, Data Scraping, FastAPI, Web Scraping, Large Language Model Operations (LLMOps), Knowledge Graphs, Agentic AI, Architecture, AI Automation, Supabase, Amazon Bedrock AgentCore, Automatic Speech Recognition (ASR), ElevenLabs Solutions, Anthropic, API Integration, Third-party Integration, Predictive Analytics, Text Classification, Minimum Viable Product (MVP), Classification, Deployment, AI Voice Agents, API Design, WebSockets, Embedding Models, Fine-tuning, Agentic AI Systems, UiPath, Pgvector, Light LLMs, AI Agent Orchestration, Workflow Automation, Agentic RAG Systems, Semantic Search, Conversational AI, Multi-agent Systems, RAG Architecture, Workflow Automation & System Integration, Decision Modeling, AI Design, Agentic Workflow Design, Model Evaluation, Agentic Coding, Audio Processing, API Gateways, Telemetry, Cursor AI, Entrepreneurship, Computer Vision, Graph Neural Networks (GNNs), CI/CD Pipelines, Team Leadership, Audio, Generative Adversarial Networks (GANs), Finance, Pinecone, Scalable Vector Databases, Distributed Systems, Real-time Data, CRM Implementation (Salesforce), Sentiment Analysis, Tf-idf, Reinforcement Learning, Deep Reinforcement Learning, Bayesian Statistics, Bayesian Inference & Modeling, Intellectual Property, Brand Management, Embeddings from Language Models (ELMo), Fake Content Detection, Natural Language Understanding (NLU), Legal Technology (Legaltech)
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