Job description

Role Summary

We are looking for a highly skilled AI Engineer to help build and scale our internal AI ecosystem. You will design and deploy production-ready AI agents and multi-agent workflows that automate complex business processes.

This role bridges LLM engineering and backend software development, using a modern stack centered on LangGraph, PydanticAI, and Model Context Protocol (MCP).

This is a hands-on role ideal for someone excited about building real-world AI systems and shipping them to production.

Key Responsibilities

Agentic Workflow Development

• Design and implement stateful AI workflows using LangGraph.

• Build role-based multi-agent collaborations using CrewAI.

• Develop reliable long-running and branching AI processes.

Structured AI Services

• Use Pydantic and PydanticAI to enforce type safety and structured outputs.

• Implement schema-driven AI pipelines and validation layers.

• Contribute to reliability, logging, and observability of AI services.

RAG & Context Engineering

• Build and maintain Retrieval-Augmented Generation (RAG) pipelines.

• Work with vector databases such as Qdrant, ChromaDB, or PgVector.

• Contribute to GraphRAG implementations using Neo4j or FalkorDB.

• Improve search quality using hybrid search and reranking techniques.

MCP & Internal Tooling

• Help build and maintain Model Context Protocol (MCP) servers.

• Integrate AI agents with internal APIs, databases, and tools.

• Support development of internal AI frameworks and reusable components.

Model Integration & Optimization

• Work with both local models (Ollama / LM Studio) and cloud LLM providers.

• Assist in model evaluation, optimization, and experimentation.

• Support domain-specific fine-tuning and benchmarking.

Performance & Scalability

• Implement semantic caching and context optimization strategies.

• Improve latency, cost efficiency, and scalability of AI services.

Deployment & Engineering

• Containerize services using Docker.

• Deploy AI workloads on AWS Lambda or GCP Cloud Functions.

• Write clean, maintainable, production-quality Python code.

Required Skills & Experience: -

• 3–6 years of experience in software engineering, ML engineering, or AI engineering.

• Hands-on experience building production applications in Python.

• Experience with LangChain or LangGraph (or similar LLM frameworks).

• Strong experience with Pydantic and structured data validation.

• Exposure to multi-agent frameworks such as CrewAI is a plus.

• Experience working with LLM APIs and prompt engineering.

• Familiarity with RAG pipelines and vector databases.

Databases

• Experience with at least one Vector DB (Qdrant / PgVector) or Graph DB (Neo4j or FalkorDB).

Backend & Infrastructure

• Strong Python (Asyncio preferred).

• Experience with Docker and cloud/serverless deployments.

• Understanding of REST or gRPC APIs.

Preferred Qualifications

• Experience with Human-in-the-Loop workflows.

• Background in semantic search or information retrieval.

• Experience building internal tools or developer platforms.

• Familiarity with model fine-tuning or evaluation.

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