Lead - Artificial Intelligence Engineer

Lead AI Engineer (Python / LLM / Agentic AI)

Job Description

We are looking for an experienced Lead AI Engineer to design, develop, and deliver production-ready AI applications and backend services.

The ideal candidate combines strong Python backend engineering skills with hands-on experience building LLM-powered applications, AI agents, and intelligent automation solutions.

Key Responsibilities

• Design, develop, and maintain scalable backend services and AI-powered applications using Python.

• Design and implement AI agents and agentic workflows , including tool calling, orchestration, multi-step reasoning, and multi-agent collaboration.

• Build LLM-powered solutions using RAG, embeddings, vector databases, and structured outputs .

• Integrate LLMs with internal systems, enterprise APIs, and external services using MCP and other integration approaches.

• Evaluate and optimize AI solutions for accuracy, reliability, performance, latency, and cost.

• Design scalable architectures using asynchronous processing, message queues, caching, and event-driven patterns.

• Implement AI security controls, including prompt injection protection, data privacy, access control, and safe tool execution.

• Build and deploy production-ready services with proper testing, monitoring, logging, and error handling.

• Collaborate with product and engineering teams to translate business requirements into technical solutions.

• Lead technical design discussions, review code, mentor engineers, and establish engineering best practices.

Requirements

Job Requirements

Required Qualifications

• 7+ years of professional software engineering experience, including at least 2 years of backend development using Python.

• Strong experience with Python, FastAPI, asynchronous programming, and Pydantic .

• Hands-on experience developing and deploying LLM-powered applications in production .

• Practical experience with AI agents, tool/function calling, and agent orchestration frameworks , such as LangGraph, LangChain, or equivalent solutions.

• Strong understanding of RAG architectures, embeddings, semantic search, and vector databases .

• Experience integrating LLMs through APIs, including structured outputs, prompt engineering, and handling model failures.

• Experience evaluating LLM applications, including response quality, hallucinations, reliability, and performance.

• Solid understanding of distributed systems, message queues, caching, retries, idempotency, and persistent state management.

• Experience with PostgreSQL, Redis, Docker, and REST APIs .

• Strong knowledge of automated testing, clean architecture, design patterns, and code quality practices.

• Experience designing and delivering major features or products from concept to production.

• Strong analytical, troubleshooting, communication, and technical leadership skills.

• Experience with Model Context Protocol (MCP) and building MCP servers or clients.

• Experience with advanced agent orchestration, multi-agent architectures, and human-in-the-loop workflows.

• Experience with Qdrant, pgvector, Pinecone , or similar vector databases.

• Experience with NATS, Kafka , or other event-driven messaging technologies.