Ai Engineering Solution Lead

Solid hands-on AI Engineering Solution Lead – not just a hands-on engineer but to also partner with client in providing thoughtful input on the possibilities for enhancing and expanding platform monitoring and SRE capabilities.

Key Responsibilities:

- Architect enterprise RAG solutions supporting large-scale knowledge retrieval and GenAI applications.
- Design retrieval frameworks including ingestion, chunking, embeddings, indexing, semantic search, reranking, and grounding.
- Establish enterprise patterns for vector search, hybrid retrieval, knowledge graphs, and AI-ready content platforms.
- Define evaluation frameworks to improve retrieval quality and response accuracy.
- Design and govern enterprise Agentic AI architectures.
- Build autonomous and human-in-the-loop workflows using tool integration, planning, reasoning, and orchestration frameworks.
- Lead development of single-agent and multi-agent systems for complex business processes.
- Establish standards for agent memory, context management, state handling, and agent observability.
- Define architecture for enterprise AI platforms, gateways, model orchestration, and shared AI services.
- Drive LLMOps practices including deployment automation, monitoring, evaluation, governance, and lifecycle management.

Required Qualifications:

- Bachelor's degree in computer science, Artificial Intelligence, Machine Learning, Engineering, Data Science, or related field.
- 12+ years of experience in AI, Machine Learning, Software Engineering, AI Platforms, or related disciplines.
- 5+ years of hands-on experience delivering Generative AI and LLM-based solutions in production environments.




- Proven experience leading complex enterprise GenAI and Agentic AI initiatives.
- Hands-on experience with RAG architectures, semantic retrieval, vector search, and enterprise knowledge systems.
- Experience building AI platforms, AI gateways, orchestration layers, and shared GenAI services.
- Experience implementing AI evaluation, observability, governance, and Responsible AI practices.
- Solid programming experience in Python and AI application development.
- Experience with Azure, AWS, or GCP AI platforms.
- Solid expertise in foundation models, transformer architectures, embeddings, tokenization, inference optimization, and context management.
- Solid understanding of Agentic AI frameworks, tool integration, workflow automation, and multi-agent systems.
- Proven excellent communication, stakeholder management, mentoring, and technical leadership skills.

Technical Skills & Experience:

- Experience with LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, CrewAI, or similar frameworks.
- Experience building enterprise AI platforms, gateways, model catalogs, and reusable AI services.
- Experience with vector databases such as Chroma and Azure AI Search.
- Experience implementing enterprise LLMOps, AI governance, AI security, and compliance frameworks.
- Experience building AI accelerators, shared frameworks,



and enterprise AI reference architectures.
- Healthcare, insurance, financial services, or other regulated industry experience.
- Expertise with Azure OpenAI, Azure AI Foundry, Bedrock, Vertex AI, Anthropic, OpenAI, and open-source model ecosystems.
- Expertise in advanced RAG architectures including hybrid search, graph retrieval, reranking, and knowledge graphs.
- Proven contributions to patents, publications, enterprise innovation programs, or AI thought leadership initiatives.

Preferred Qualifications:

- Advanced degree in Artificial Intelligence, Machine Learning, Computer Science, or related field.
- Experience leading small teams of GenAI engineers, architects, or specialists.
- Experience with LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, CrewAI, or similar frameworks.
- Experience building enterprise AI platforms, gateways, model catalogs, and reusable AI services.
- Experience with vector databases such as Chroma and Azure AI Search.
- Experience implementing enterprise LLMOps, AI governance, AI security, and compliance frameworks.
- Experience building AI accelerators, shared frameworks, and enterprise AI reference architectures.
- Healthcare, insurance, financial services, or other regulated industry experience.
- Expertise with Azure OpenAI, Azure AI Foundry, Bedrock, Vertex AI, Anthropic, OpenAI, and open-source model ecosystems.
- Expertise in advanced RAG architectures including hybrid search, graph retrieval, reranking, and knowledge graphs.
- Proven contributions to patents, publications, enterprise innovation programs, or AI thought leadership initiatives.

📌 AI Engineering Solution Lead (Hyderabad)
🏢 HCL INDIA
📍 Hyderabad