Lead Software Engineer (Python, GenAI, LLM)

We are seeking an experienced AI Engineer to design and build production-grade Generative AI solutions with a strong focus on agentic workflows, multi-agent systems, and enterprise AI applications. The ideal candidate combines strong software engineering fundamentals with hands-on expertise in LLMs, RAG architectures, and AI agent frameworks.

Responsibilities

• Design, architect, and develop scalable Generative AI applications and agentic solutions for real-world business use cases
• Build and orchestrate AI agents using frameworks such as LangChain, LangGraph, Google ADK, CrewAI, AutoGen, Microsoft Copilot Studio, or similar technologies
• Develop and maintain backend services, APIs, microservices, and data pipelines that power AI-driven products
• Implement advanced AI patterns including RAG, Agentic RAG, tool/function calling, planning & reflection loops, and human-in-the-loop workflows
• Engineer and optimize prompts, system instructions, and agent workflows to improve reliability, accuracy, and user experience
• Integrate LLMs with enterprise systems, third-party APIs, vector databases, and knowledge repositories
• Monitor, evaluate, and continuously improve model and agent performance using observability tools, metrics, and user feedback
• Collaborate closely with Product, Engineering, Data, and Design teams to deliver impactful AI solutions
• Stay current with emerging AI technologies, frameworks, and best practices, contributing innovative ideas to the team
• Document architectures, design decisions, and reusable solution patterns while supporting knowledge sharing across teams

Requirements

• 7 to 12 years of relevant professional experience
• Hands-on experience building applications using Generative AI and LLM technologies
• Strong proficiency in Python and experience developing production-ready applications
• Hands-on experience with at least two agentic AI frameworks such as LangChain, LangGraph, Google ADK, CrewAI, AutoGen, or Microsoft Copilot extensibility
• Experience with cloud AI platforms including Azure OpenAI, AWS Bedrock, or Google Vertex AI/ADK
• Strong backend development skills, including REST/gRPC APIs, asynchronous programming, Docker, and frameworks such as FastAPI or Flask
• Solid understanding of leading LLMs including OpenAI GPT models, Anthropic Claude, Google Gemini, and open-source alternatives
• Practical experience building RAG solutions using vector databases such as Pinecone, Weaviate, ChromaDB, or Qdrant
• Expertise in prompt engineering, LLM orchestration, structured outputs, guardrails, ReAct patterns, and evaluation techniques
• Strong problem-solving, system design, and architectural decision-making skills
• Excellent communication skills with the ability to collaborate effectively across global teams