Sr GenAI Engineer

We are seeking an accomplished Senior Generative AI Engineer to lead initiatives in designing, building, and deploying cutting-edge AI solutions. This role demands strong expertise in RAG (Retrieval-Augmented Generation), MCP (Model Context Protocol), Agentic AI systems, and QLoRA-based fine-tuning. The ideal candidate is a leader in AI governance, capable of integrating both open-source (LLaMA, Gemma, Mistral, Falcon, etc.) and closed-source (OpenAI, Anthropic, Gemini, etc.) models into production systems.

Location:

Experience Level:

    Lead the design, development, and deployment of GenAI applications using frameworks such as LangChain, LlamaIndex, HuggingFace Transformers.

    Architect and implement RAG pipelines, agentic systems, and MCP-based integrations.

    Drive QLoRA-based fine-tuning and quantization for domain-specific tasks.

    Ensure AI governance, compliance, and responsible use of generative AI solutions.

    Evaluate, benchmark, and integrate open-source and proprietary LLMs for enterprise-grade use cases.

    Collaborate with cross-functional teams including data engineers, backend developers, and product stakeholders.

    Mentor junior engineers, fostering growth in prompt engineering, model experimentation, and best practices.

    Stay ahead of emerging research and incorporate the latest advancements in LLMs, embeddings, multimodal models, and vector databases.

    6+ years in software/ML engineering, with 2+ years focused on Generative AI.

    Deep expertise in RAG, MCP, agentic architectures, and LLMs.

    Strong proficiency in Python and leading AI frameworks (LangChain, LlamaIndex, HuggingFace).

    Proven hands-on experience with QLoRA fine-tuning and inference optimization.

    Solid knowledge of AI governance frameworks, compliance, and ethical AI practices.

    Familiarity with cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes).

    Exceptional communication and leadership skills, with the ability to mentor teams and articulate complex concepts clearly.

    Prior work with multi-agent systems, memory-based agents, or frameworks such as CrewAI, AutoGen, LangGraph.

    Experience with multimodal models (text-to-image/video/audio).

    Research publications or open-source contributions in the GenAI ecosystem.

    Demonstrated thought leadership in emerging AI technologies and enterprise adoption.