Full Stack AI Engineer

Position: Full Stack AI Engineer

Job Type: Contract to Hire(C2H)

Experience: 3+ Years
Location: Bangalore & Chennai
Joining: Immediate Joiners Preferred

Role Overview

We are building autonomous, LLM-powered agents in-house from the ground up, with no off-the-shelf vendor platform. Working alongside senior engineers, you will design, build, and ship agents that reason over internal data, call tools and APIs, and carry multi-step tasks through to completion. This is a hands-on build role where you will write production-grade Python every day and see your agents go live.

Key Responsibilities

• Build LLM-powered agents from scratch, including prompt design, tool/function calling, memory, and multi-step task orchestration.
• Connect agents to internal databases and APIs, including SQL and data-access layers.
• Implement retrieval pipelines (RAG), including chunking, embeddings, vector search, and relevance tuning.
• Build evaluation harnesses to measure agent accuracy, latency, cost, and failure modes.
• Implement guardrails, retries, error handling, and tracing to ensure predictable agent behavior in production.
• Deploy and monitor agents on cloud infrastructure in collaboration with platform and data teams.

Essential Skills

• 34 years of software engineering experience with strong, production-grade Python.
• At least one LLM application built beyond tutorial level, such as a chatbot, RAG system, or agent, with the ability to explain design decisions.
• Working knowledge of LLM APIs such as OpenAI, Anthropic, or open-source models, including function/tool calling and structured outputs.
• Strong SQL and relational database fundamentals, including schema design, joins, and query performance.
• REST API development and integration using FastAPI, Flask, or equivalent.
• Strong Git knowledge and familiarity with Docker.
• Practical understanding of LLM agent failure modes, including hallucinations, context limits, tool-call errors, and runaway loops.

Cloud & Data Stack

Azure

• Azure OpenAI Service
• Azure AI Foundry
• Azure AI Search
• Azure Functions
• Cosmos DB
• Azure SQL
• Azure Container Apps

AWS

• Amazon Bedrock
• Amazon OpenSearch or Kendra
• AWS Lambda
• DynamoDB
• RDS/Aurora with pgvector
• ECS/Fargate

Vector Stores

• pgvector
• Pinecone
• Qdrant
• Weaviate
• Chroma

Desirable Skills

• Agent and orchestration frameworks: LangGraph, LangChain, CrewAI, AutoGen, or Semantic Kernel.
• Multi-agent workflows, Model Context Protocol (MCP), async, or streaming architectures.
• LLM observability and evaluation tools such as LangSmith, Langfuse, Arize, or Ragas.
• CI/CD, Infrastructure as Code using Terraform or Bicep, or MLOps/LLMOps deployment experience.
• TypeScript or Node.js for product-facing agent interfaces.
• Open-source contributions or a public portfolio.