Seeking a senior lead to drive Generative AI initiatives within the Pharma Regulatory domain. This role requires extensive experience in data science, AI, ML, and GenAI technologies, with a specific focus on application development. You will leverage cutting-edge frameworks and LLMs to solve complex problems and deliver innovative solutions.

The ideal candidate is a strategic problem-solver with a passion for generative AI and a proven track record in developing and deploying AI/ML models. You will collaborate with cross-functional teams and business stakeholders to translate requirements into impactful technological advancements.

Develop and implement advanced GenAI applications and solutions, particularly within the pharmaceutical regulatory landscape.

Lead the design and development of technical solutions using Agentic AI frameworks, including LangGraph, LlamaIndex, and MCP.

Utilize expertise in both paid and open-source LLMs, VLMs, and SLMs, applying techniques like RAG, CAG, and Agents.

Manage and process large volumes of unstructured and structured data, including data lakes.

Develop and deploy AI models efficiently using Python and FastAPI, ensuring scalability and performance.

Collaborate with engineering teams to deploy and optimize generative models in production environments.

Work closely with business stakeholders to articulate AI concepts and manage model behavior analysis.

A minimum of 8 years of experience in Data Science, AI/ML, and GenAI technologies, with at least 2 years specifically in GenAI application development.

Expertise in Agentic AI frameworks (LangGraph, LlamaIndex, MCP) and experience with paid (OpenAI on Azure, AWS Bedrock) and open-source LLMs.

Proficiency in RAG techniques, Agents, MCP, and NLQ to SQL, alongside experience with large-scale data handling (unstructured and structured).

Strong Python programming skills, with familiarity in FastAPI, and a solid understanding of model development, serving, and training.

Extensive experience with cloud ecosystems (AWS/Azure), Kubernetes, serverless deployments, and prompt engineering techniques.

Demonstrated experience in developing LLM guardrails and working with databases such as RDBMS, Data Lake, PostgreSQL, MarkLogic, and Elasticsearch.