Lead Software Engineer - Python

Job description

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