Senior / Lead GenAI Engineer - Data and AI

Job description

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 strong 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.