I help startups and enterprises build, deploy, and scale AI-powered systems — from RAG pipelines and LLM integrations to cloud infrastructure and DevOps automation.
What I Do
End-to-end delivery — from system design to production deployment.
Production-grade AI systems — RAG pipelines, LLM integrations, AI agents, and intelligent automation. From prototype to deployed product.
Take ML models from notebook to production. Model serving, monitoring, versioning with MLflow, and end-to-end pipelines using Airflow and cloud platforms.
Design and implement CI/CD pipelines, containerize applications with Docker, orchestrate with Kubernetes, and automate infrastructure with Terraform and IaC.
Architect scalable, cost-efficient infrastructure on AWS, Azure, and GCP. Cloud migration, modernization, and ongoing optimization for performance and cost.
High-performance backend systems using Python and FastAPI. Async processing, message queues (RabbitMQ/Kafka), database design, and scalable API development.
End-to-end data pipelines and transformation workflows. Snowflake, Airflow, SQL stored procedures, and seamless integration with downstream ML models.
Work
Real systems built and shipped.
Production-grade travel itinerary generator using Retrieval-Augmented Generation. Custom knowledge base via FAISS vector store, intelligent fallback to general LLM knowledge, containerized with Docker and deployed to cloud.
Real-time MLOps dashboard for monitoring model training runs. Integrates with MLflow for experiment tracking, backed by PostgreSQL and Redis caching, with live metric updates via multithreading.
Automated data pipeline for promotions analytics. Complex SQL transformations in Snowflake, orchestrated via Apache Airflow, outputs feeding directly into ML model pipelines.
High-throughput async data ingestion API with customizable parsing and standardization mechanisms. Built for diverse data sources with asynchronous task processing via message queues.
About
I'm Pritam Bhutada — an AI Engineer and DevOps Architect with 8+ years of experience building and deploying intelligent systems at scale.
My work spans the full stack of modern AI delivery: designing RAG-based applications, integrating LLMs into production systems, building MLOps pipelines, and deploying everything on cloud infrastructure with proper CI/CD, containerization, and monitoring.
I work with startups and SMBs who want to move fast without breaking things — bringing the engineering discipline of enterprise teams to organizations that need results, not overhead.
Work With MeWriting
Deep dives on system design, AI engineering, and backend architecture.
A deep dive into designing a recommendation system that scales to billions of interactions — architecture decisions, trade-offs, and production considerations.
Read on Medium →How to evolve a news feed from a simple monolith to a distributed system that aggregates and personalizes content at scale for social platforms.
Read on Medium →Exploring notification systems as essential infrastructure in connected applications — architecture patterns for delivering millions of notifications reliably.
Read on Medium →Real-time communication patterns for live chat, notifications, and collaborative dashboards — building distributed WebSocket systems that actually hold up under load.
Read on Medium →A practical look at consistent hashing — why naive approaches fail under node changes, and how consistent hashing keeps distributed systems stable at scale.
Read on Medium →In microservices and scalable APIs, a robust rate limiter is a necessity. A complete implementation using FastAPI, Redis, and Lua scripts for atomic operations.
Read on Medium →How Retrieval-Augmented Generation overcomes LLM limitations for factual accuracy — building and deploying a full RAG pipeline from scratch to production.
Read on Medium →Contact
Have a project in mind? Let's talk.