Gen AI Engineer ( ML )

As a Gen AI Engineer (ML) at our company, your role will involve leading the design, development, and deployment of GenAI applications using frameworks like LangChain, LlamaIndex, and HuggingFace Transformers. You will be responsible for architecting and implementing RAG pipelines, agentic systems, and MCP-based integrations. Additionally, you will drive QLoRA-based fine-tuning and quantization for domain-specific tasks, ensuring AI governance, compliance, and responsible use of generative AI solutions. Your expertise will be crucial in evaluating, benchmarking, and integrating open-source and proprietary LLMs for enterprise-grade use cases. Collaboration with cross-functional teams including data engineers, backend developers, and product stakeholders will be key, along with mentoring junior engineers to foster growth in prompt engineering, model experimentation, and best practices. Staying ahead of emerging research and incorporating the latest advancements in LLMs, embeddings, multimodal models, and vector databases will also be part of your responsibilities. Key Responsibilities: - Lead the design, development, and deployment of GenAI applications using frameworks like LangChain, LlamaIndex, and 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. Qualifications Required: - 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. In addition to the above details from the job description, there are no additional details of the company mentioned.,