Ekfrazo Technologies - Lead AI Engineer - Generative AI in Bangalore, भारत

Lead AI EngineerExperience : 9+ YearsLocation : Bangalore & NoidaEmployment Type : Full-TimeRole Overview : We are looking for a Lead AI Engineer with strong hands-on experience in Generative AI, Agentic AI, Python, and Machine Learning to lead the design and development of intelligent AI-powered https://jobeax.com/link/5WM6jVJPxXxkaX5M ideal candidate should have experience building and deploying LLM-based applications, AI agents, RAG pipelines, and machine learning solutions in enterprise https://jobeax.com/link/m7FLZdCihDPQzn7w candidate will work closely with product, engineering, and business teams to translate business problems into scalable AI https://jobeax.com/link/RdASFfLzdKSOHnSh Skills : - Generative AI / Agentic AI- Python- Machine LearningKey Responsibilities : - Lead the architecture, development, and deployment of Generative AI and Agentic AI solutions.- Design and build LLM-powered applications, AI agents, copilots, and intelligent automation solutions.- Develop scalable RAG architectures using enterprise data sources and vector databases.- Build AI agents capable of reasoning, planning, tool usage, decision-making, and task execution.- Develop and integrate LLMs from providers such as OpenAI, Azure OpenAI, Anthropic, Google Gemini, or open-source models.- Design and implement prompt engineering, structured outputs, function/tool calling, and agent workflows.- Apply Machine Learning and NLP techniques to solve complex business problems.- Develop Python-based AI/ML applications and APIs.- Work with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, or similar.- Implement model evaluation, monitoring, guardrails, hallucination reduction, and responsible AI practices.- Collaborate with Data Scientists, ML Engineers, Software Engineers, Architects, and business stakeholders.- Lead technical discussions, conduct code/design reviews, and mentor junior AI/ML engineers.- Optimize AI solutions for performance, scalability, reliability, cost, and latency.- Support production deployment of AI/ML solutions using cloud and DevOps/MLOps practices. (ref:hirist.tech)