Looking for a job

Sai Teja Kamoji

@saitejakamoji

AI Development & Support Engineer at Freddie Mac who architected multi-agent workflows that improved requirement analysis and code review accuracy by 45%.

United States

Message

At Freddie Mac, I architect multi-agent workflows using local LLMs and prompt engineering, improving requirement analysis and code review accuracy by 45%.

I also engineer Python-based MCP servers and deploy local LLMs with vLLM and Ollama to support AI-driven requests.

At Walmart, I developed a multimodal RAG pipeline with OpenSearch Vector and FastAPI that improved retrieval precision by over 40%. I also built Python microservices for document ingestion and instrumented them with OpenTelemetry.

At Tech Mahindra, I developed REST APIs with Java and Spring Boot and built distributed Kafka pipelines. I also worked on Python OCR pipelines and containerized microservices for storage and HPC workflows.

Experience

Work history, roles, and key accomplishments

Current

Software Engineer - AI

Current

Freddie Mac

Mar 2026 - Present (6 months)

Architected multi-agent workflows using local LLMs and prompt engineering, improving requirement analysis and code review accuracy by 45%. Engineered Python-based MCP servers and deployed local LLMs using vLLM and Ollama, supporting over 10,000 AI-driven requests daily.

Local LLMs Prompt Engineering Agent Architectures Python MCP Servers VLLM Ollama Linux Debugging

Software Engineer

Walmart

Oct 2024 - Mar 2026 (1 year 5 months)

Developed a multimodal RAG pipeline using OpenSearch Vector and FastAPI, improving retrieval precision by over 40%. Built Python microservices with REST API integration and Docker deployment, and instrumented services with OpenTelemetry to reduce latency by 25%.

RAG OpenSearch fastAPI Python Microservices REST APIs Docker OpenTelemetry Kubernetes CI CD Java Agent Architectures Prompt Engineering Vector Databases

Software Engineer

Tech Mahindra

Sep 2021 - Jun 2024 (2 years 9 months)

Developed over 15 REST APIs with Java and Spring Boot, and built distributed Kafka pipelines with OpenTelemetry tracing processing 1 million+ events daily. Constructed Python OCR pipelines and deployed containerized microservices, reducing deployment times by 40%.