Senior / Lead GenAI Engineer - Data and AI

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-solv…