Gen AI Lead
Posted Apr 13, 2026
Key Responsibilities:
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Lead end-to-end development of Generative AI solutions using Large Language Models (LLMs) for enterprise use cases such as chatbots, document intelligence, summarization, search, and automation. - Architect and implement Gen AI applications using prompt engineering, RAG (Retrieval Augmented Generation), fine-tuning, agent-based frameworks, and MCP (Model Context Protocol) for secure tool and data integration. - Evaluate and integrate LLMs from various sources such as OpenAI, Azure OpenAI, Anthropic, Google, and open-source models like LLaMA and Mistral. - Design scalable Gen AI architectures including model orchestration, vector databases, embeddings, and APIs. - Guide teams on best practices for prompt design, model evaluation, performance optimization, and cost control. - Collaborate with business stakeholders to translate requirements into Gen AI solutions. - Ensure responsible AI practices including data privacy, security, bias mitigation, and governance. - Own performance and value metrics for Gen AI platforms, balancing model accuracy, response latency, and operational cost while ensuring alignment with business priorities. - Mentor junior engineers and provide technical leadership in Gen AI initiatives. - Stay current with Gen AI trends, tools, and frameworks and recommend adoption strategies. Qualification Required:
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6-8 years of overall experience with 4+ years in AI/ML or Gen AI-related roles. - Strong hands-on experience with LLMs and Gen AI frameworks like LangChain, Langraph, LlamaIndex, Semantic Kernel, and CrewAI. - Proficiency in Python with hands-on experience in deep learning frameworks (PyTorch or TensorFlow) and experience building production-grade APIs (FastAPI/Flask). - Solid understanding of RAG pipelines, embeddings, vector databases such as FAISS, Pinecone, Weaviate, and Chroma. - Experience with prompt engineering, model evaluation, and fine-tuning techniques. - Working knowledge of cloud platforms (Azure/AWS/GCP), preferably Azure OpenAI. - Familiarity with MLOps/LLMOps concepts, CI/CD, monitoring, and observability. - Strong problem-solving, communication, and stakeholder management skills. Key Responsibilities: