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I’m a Full Stack Engineer who builds production-oriented AI systems—combining reliable backend logic with LLM-powered features. I work with Python and FastAPI for APIs, React/Next.js for frontend experiences, and PostgreSQL for structured data, ensuring everything is validated, tested, and failure-aware. I specialize in LLM application engineering such as RAG pipelines, multi-agent workflows, tool calling, and evaluation/observability practices. My goal is to ship deterministic, dependable AI products where structured outputs and automated tests strengthen the overall system beyond prompt-based behavior.…I’m a Full Stack Engineer who builds production-oriented AI systems—combining reliable backend logic with LLM-powered features. I work with Python and FastAPI for APIs, React/Next.js for frontend experiences, and PostgreSQL for structured data, ensuring everything is validated, tested, and failure-aware. I specialize in LLM application engineering such as RAG pipelines, multi-agent workflows, tool calling, and evaluation/observability practices. My goal is to ship deterministic, dependable AI products where structured outputs and automated tests strengthen the overall system beyond prompt-based behavior.

Srinath Doggala

Full Stack Developer, AI Engineer, Software Engineer, +4

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I’m a Full Stack Engineer who builds production-oriented AI systems—combining reliable backend logic with LLM-powered features. I work with Python and FastAPI for APIs, React/Next.js for frontend experiences, and PostgreSQL for structured data, ensuring everything is validated, tested, and failure-aware. I specialize in LLM application engineering such as RAG pipelines, multi-agent workflows, tool calling, and evaluation/observability practices. My goal is to ship deterministic, dependable AI products where structured outputs and automated tests strengthen the overall system beyond prompt-based behavior.…I’m a Full Stack Engineer who builds production-oriented AI systems—combining reliable backend logic with LLM-powered features. I work with Python and FastAPI for APIs, React/Next.js for frontend experiences, and PostgreSQL for structured data, ensuring everything is validated, tested, and failure-aware. I specialize in LLM application engineering such as RAG pipelines, multi-agent workflows, tool calling, and evaluation/observability practices. My goal is to ship deterministic, dependable AI products where structured outputs and automated tests strengthen the overall system beyond prompt-based behavior.

Available to hire

I’m a Full Stack Engineer who builds production-oriented AI systems—combining reliable backend logic with LLM-powered features. I work with Python and FastAPI for APIs, React/Next.js for frontend experiences, and PostgreSQL for structured data, ensuring everything is validated, tested, and failure-aware.

I specialize in LLM application engineering such as RAG pipelines, multi-agent workflows, tool calling, and evaluation/observability practices. My goal is to ship deterministic, dependable AI products where structured outputs and automated tests strengthen the overall system beyond prompt-based behavior.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Language

Javanese

Intermediate

Work Experience

Founding AI Full Stack Engineer Intern at Sreeva AI

May 1, 2026 - Present

Built and shipped production AI features using Python, FastAPI, React, Next.js, PostgreSQL, Redis, Docker, and LLM APIs—owning functionality across backend and frontend. Designed REST APIs with request/response validation, authentication, async workflows, and SQL-backed logic with explicit error handling for production failure modes. Developed RAG and multi-step LLM workflows using LangChain and LangGraph, combining retrieval, tool calling, and validation with deterministic application logic to improve reliability of AI outputs. Implemented robust failure handling patterns across provider health issues, timeouts, and malformed tool arguments to prevent cascading failures in agent workflows.