Senior / Lead GenAI Engineer - Data and AI (Bengaluru)

Role & responsibilities We are looking for a Senior / Lead GenAI Engineer to design and deploy production-grade AI systems for enterprise use cases. In this role, you will build LLM-powered applications such as retrieval-augmented systems, agentic workflows, with a strong focus on scalability, reliability, and real-world impact. Also, you will work on internal product development and support client-focused projects, with exposure to the pharma domain being a plus. You'll collaborate with experienced team members and learn to create impactful AI solutions while optimizing and developing GenAI applications. Your responsibilities will include - Supporting the design, development, and deployment of GenAI solutions, learning to address challenges like hallucinations, bias, and latency, while contributing to performance and reliability improvements. - Collaborating with both internal teams and external stakeholders, particularly in the pharma space, to understand business requirements and contribute to the development of tailored AI-powered systems. - Assisting in the full lifecycle of AI project delivery, including ideation, model fine-tuning, deployment, and performance monitoring under the guidance of senior team members. - Learning and applying fine-tuning techniques (such as LoRA, PEFT) to Large Language Models (LLMs) for specific business needs. - Assisting in the development of scalable pipelines for AI model deployment, including handling error management, monitoring, and retraining strategies. - Actively participating in a collaborative environment, sharing ideas and working as part of a dynamic team of data scientists and AI engineers. What do we expect Key Responsibilities: GenAI & Agentic AI Development - Design and deploy NLP and GenAI solutions using LLMs, fine-tuning techniques (LoRA, PEFT), and AI-powered automation. - Build and optimize LLM-powered chatbots, virtual assistants, and AI agents with high accuracy and contextual awareness. - Implement agentic AI systems enabling autonomous, multi-step workflows using orchestration frameworks (LangChain, LlamaIndex, AutoGen, etc.). Architecture & Pipelines - Architect scalable NLP pipelines covering text preprocessing, entity recognition (NER), summarization, Q&A;, and conversational AI. - Lead research and implementation of advanced GenAI techniques: LLMs, Agentic Solutioning, transformers, embeddings, RAG, and multi-modal models. - Optimize inference pipelines using quantization, model distillation, and retrieval-enhanced generation for performance and cost efficiency. Production & MLOps - Develop scalable model deployment pipelines with robust error handling, monitoring, and retraining strategies. - Ensure LLM observability and guardrails - covering model monitoring, safety, fairness, and regulatory compliance. - Lead MLOps practices: CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments on AWS, GCP, or Azure. Collaboration & Delivery - Collaborate with cross-functional teams to integrate GenAI solutions into enterprise applications via robust APIs and microservices. - Work with internal teams and external stakeholders (including pharma clients) to translate business requirements into AI solutions. - Mentor junior engineers, champion AI best practices, and contribute to AI governance frameworks in regulated industries. Preferred candidate profile - 5-7.5 years in NLP, Generative AI, or related ML discipline. - - 2+ years working with GenAI/LLMs in production systems. - - Deep expertise in LLMs, transformer architecture, Agentic Frameworks and fine-tuning techniques. - - Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER). - - Experience with retrieval-augmented generation (RAG), vector databases, (FAISS, Pinecone, ChromaDB), and prompt engineering. - - Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails. - - Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise. - - Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies. - - Prior experience in life sciences, pharma, or other regulated industries is a plus. - - A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers. What makes this role exciting - Opportunity to work on real-world, production-grade GenAI systems at enterprise scale. - Exposure to diverse industries and high-impact problem statements. - A robust ecosystem of AI practitioners, accelerators, and innovation-led culture. - Ability to shape next-generation AI solutions beyond prototypes. *If you have expertise in any of the above skills then let us know and we will align opportunities with your strengths and support upskilling. Role & responsibilities We are looking for a