Priyanshu is available for hire
Hire PriyanshuPriyanshu Agarwal
Verified Expert in Engineering
Software Engineer and Developer
Bengaluru, India
Toptal member since August 12, 2026
Bio
Priyanshu is a senior software engineer with over 5 years of experience building distributed back-end systems and AI-powered enterprise applications for clients, including Aereo and Sales Handy. His primary expertise is in Python, distributed systems, and back-end architecture for technology and mining industries, where Priyanshu thrives in cloud-native infrastructure environments. He reduced distributed processing costs by 90% while at Aereo.
Portfolio
Aereo
Python, Kubernetes, Terraform, LangChain, LangGraph...
Aereo
Python, Django, Terraform, GitLab CI/CD, Microservices, APIs...
Aereo
AWS Batch, Django, Python, TypeScript, React, Microservices, APIs...
Experience
- AWS IoT - 5 years
- React - 5 years
- Python - 5 years
- Microservices - 5 years
- Django - 5 years
- GitLab CI/CD - 5 years
- Retrieval-augmented Generation (RAG) - 1 year
- LangGraph - 1 year
Preferred Environment
Kubernetes, Terraform, Docker, GitLab CI/CD, LangChain, LangGraph, AI/ML Workloads, AWS IoT, Python, React
The most amazing...
...platform I've built is a distributed geospatial analytics system that processed 50,000+ high-resolution images per workflow and cut costs by 90%.
Work Experience
Senior Software Development Engineer
2024 - PRESENT
Aereo
- Led engineering execution across Aereo Cloud Web, Aereo Go Mobile, and the infrastructure platform team, driving architecture, technical direction, and cross-functional delivery across back-end, front-end, and platform initiatives.
- Led and shipped Aereo Go v1, Aereo’s first dedicated mobile product, coordinating back-end, mobile, and DevOps teams from inception through production launch.
- Architected and delivered Aereo Cloud-in-a-Box, enabling secure air-gapped enterprise deployments for UAE mining customers through Kubernetes-based on-premise infrastructure and cloud-agnostic runtime abstractions.
- Designed a CloudManager Abstract Factory unifying AWS, Azure, and Kubernetes integrations behind a single back-end interface, enabling cloud-agnostic enterprise deployments and reducing platform coupling.
- Built production AI assistant workflows using LangChain, LangGraph, and RAG pipelines over geospatial datasets; designed deterministic inference orchestration and context management workflows for constrained-context LLM systems.
- Led a cross-functional team of 6 engineers and a data science team to deliver HRA and Smart Detect AI features end-to-end — including a Docker-outside-Docker GPU POC to enable AI workloads on constrained on-premise infrastructure.
- Designed and scaled distributed photogrammetry systems handling 50,000+ high-resolution images per workflow; resolved production failures involving 50GB+ orthomosaic processing and terrain generation pipelines.
- Designed and shipped Datamine Plugin v2, enabling bi-directional synchronization between Aereo Cloud and Datamine Studio, unblocking a critical enterprise mining integration.
- Reduced infrastructure provisioning time by 97% through reusable multi-environment Terraform provisioning workflows integrated with CI/CD deployment systems.
- Established structured onboarding through Aereo LMS; conducted interviews and mentored engineers across SDE-1 through SDE-3 and DevOps hiring pipelines.
Technologies: Python, Kubernetes, Terraform, LangChain, LangGraph, Retrieval-augmented Generation (RAG), Docker, CI/CD Pipelines, Microservices, Distributed Processing, AI Integration, APIs, Amazon Web Services (AWS), Architecture, Data Modeling, Large Language Models (LLMs), PostgreSQL
Software Development Engineer
2022 - 2024
Aereo
- Delivered 10+ major features in 3 months for enterprise onboarding of Coal India Limited, including 3D digitization workflows, authentication systems, distributed geospatial APIs, and terrain-processing pipelines.
- Reduced terrain-processing AWS costs by 90% by redesigning distributed synchronization pipelines using multiprocessing, threaded workers, and direct S3 streaming—eliminating EFS caching overhead entirely.
- Architected and deployed Terraform IaC infrastructure from scratch, covering ECS, RDS Multi-AZ, EC2, CloudFront, NAT Gateway, and Secrets Manager; hardened production AWS network with private subnets, Jump Servers, and IAM policies.
- Improved deployment turnaround time by 50% by setting up autoscaled GitLab Runner infrastructure from scratch, sustaining 10-15 parallel pipelines with automated CI/CD execution and self-hosted Terraform deployments.
- Built end-to-end testing infrastructure from scratch with dedicated CI/CD integration, improving deployment confidence and catching regressions before enterprise releases.
- Architected scalable asynchronous back-end workflows using Python, Django, and distributed task orchestration. Designed flexible REST APIs with dynamic response shaping to support complex front-end workflows.
- Implemented authentication systems, SSO integration, security headers, and resolved VAPT findings across distributed back-end services and enterprise deployments.
- Refactored large-scale geospatial processing architecture, improving throughput, scalability, and operational reliability across production systems.
Technologies: Python, Django, Terraform, GitLab CI/CD, Microservices, APIs, Amazon Web Services (AWS), Architecture, Data Modeling, PostgreSQL
Software Development Engineer – 1
2021 - 2022
Aereo
- Unblocked Tata Steel enterprise deployment directly by implementing secure self-hosted geospatial delivery systems satisfying government data residency requirements. Single-handedly managed all infrastructure communication and v1 rollout.
- Built terrain tile generation systems, COG generators (benchmarked EC2 instance types for performance/cost), MBTiles extraction workflows, and vector tile servers for large-scale geospatial visualization.
- Built S3-to-S3 export Lambda enabling ORI/DSM data portability across processing and analytics platforms; designed foundational event-driven AWS Batch job orchestration systems.
- Architected foundational front-end systems adopted as team-wide engineering standards; published the team’s first private npm module (@aus-platform/cesium) for scalable geospatial front-end development.
Technologies: AWS Batch, Django, Python, TypeScript, React, Microservices, APIs, Amazon Web Services (AWS), Architecture, Data Modeling, PostgreSQL
Software Engineer
2021 - 2021
Sales Handy
- Developed back-end APIs and distributed campaign management workflows using NestJS, TypeORM, and PostgreSQL.
- Optimized high-traffic SQL queries, reducing production database load and improving API responsiveness for customer-facing workflows.
- Introduced automated testing infrastructure using Jest, improving deployment reliability and engineering confidence.
Technologies: NestJS, TypeORM, PostgreSQL, Jest, Microservices, APIs, Amazon Web Services (AWS), Architecture, Data Modeling
Experience
Stock Finder
https://stock-finder.shop/Stock Finder is a hyperlocal marketplace that lets buyers in India find which nearby kirana and retail shops actually have an item in stock, instead of calling around or traveling shop to shop. Shop owners maintain a lightweight catalog; buyers search by item and see ranked results by distance, with a direct link to the owner.
I built the product end to end. On the back end, I designed a domain-split Django 5 monolith (auth/OTP, shops, inventory, leads) that exposes a REST API, with PostGIS for geospatial proximity search and a hybrid Postgres search layer that combines trigram similarity and full-text search so misspelled or partial product names still match. Redis caches nearby-shop lookups, and Celery handles asynchronous work such as image variant generation and stale-inventory sweeps. Cross-app coupling is mediated through Django signals and Redis pub/sub, which surface as real-time updates to the client over SSE.
On the front end, I built a React 19 + TypeScript SPA with Chakra UI, TanStack Query, and MapLibre GL for map-based shop discovery. I also containerized the stack and wrote the Helm charts for Kubernetes deployment.
I built the product end to end. On the back end, I designed a domain-split Django 5 monolith (auth/OTP, shops, inventory, leads) that exposes a REST API, with PostGIS for geospatial proximity search and a hybrid Postgres search layer that combines trigram similarity and full-text search so misspelled or partial product names still match. Redis caches nearby-shop lookups, and Celery handles asynchronous work such as image variant generation and stale-inventory sweeps. Cross-app coupling is mediated through Django signals and Redis pub/sub, which surface as real-time updates to the client over SSE.
On the front end, I built a React 19 + TypeScript SPA with Chakra UI, TanStack Query, and MapLibre GL for map-based shop discovery. I also containerized the stack and wrote the Helm charts for Kubernetes deployment.
Education
2017 - 2020
Bachelor's Degree in Computer Science
S.G.T.B Khalsa College - Delhi, India
Skills
Libraries/APIs
React, REST APIs, vLLM, PyTorch
Tools
GitLab CI/CD, AWS Batch, Terraform
Languages
Python, TypeScript, JavaScript, SQL
Frameworks
Django, LangGraph, NestJS, Jest, Redux
Platforms
AWS IoT, Amazon Web Services (AWS), Kubernetes, Docker
Storage
PostgreSQL, PostGIS, Redis
Paradigms
Microservices
Other
APIs, Architecture, Data Modeling, Distributed Processing, AI Integration, Large Language Models (LLMs), LangChain, Retrieval-augmented Generation (RAG), CI/CD Pipelines, TypeORM, FastAPI, Pgvector, AI, AI/ML Workloads
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