Architect scalable GenAI systems across content generation, business intelligence, recommendations, automation, and agentic workflows
Design multi-LLM and multi-agent systems using frameworks such as LangGraph, CrewAI, AutoGen, or Semantic Kernel
Lead the design and development of agentic AI systems that can reason, use tools, call APIs, manage state, and complete multi-step tasks
Design and improve RAG pipelines using vector databases, embeddings, chunking strategies, metadata, reranking, and retrieval evaluation
Define AI quality evaluation frameworks for generated content, summaries, recommendations, and agent outputs
Work with engineering and platform teams to ensure AI systems are deployable, observable, and maintainable in production
Mentor GenAI engineers and guide technical decision-making across AI initiatives

6+ years of overall engineering, AI, ML, or data product experience
3+ years of hands-on experience with GenAI, LLMs, NLP, ML systems, or AI-powered products
Core GenAI Expertise
Strong hands-on experience building production GenAI or LLM-powered systems
Deep understanding of LLMs, SLMs, prompt engineering, structured outputs, tool calling, and function calling
Experience with at least two major LLM ecosystems such as OpenAI, Gemini, Anthropic, or Hugging Face
Strong understanding of embeddings, semantic search, retrieval quality, and context design
Agentic AI Experience
Hands-on experience with agentic AI frameworks such as LangGraph, CrewAI, AutoGen, or Microsoft Semantic Kernel
Experience building multi-step reasoning workflows, agent pipelines, or AI workflow automation
Understanding of agent memory, tool integration, state management, and failure handling
Strong Python skills with experience in AI/ML or backend development

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