Lead the design and development of agentic AI applications that enable scientists
to explore experimental, proprietary, and research data to generate actionable scientific insights.
Own end-to-end delivery of production-grade AI products, including agentic AI systems, fine-tuning, RAG pipelines, vector databases with Python and PyTorch.

Key responsibilities:
• Designing and implementing conversational AI agents using modern LLM platforms and agentic architectures
• Developing RAG systems that connect LLMs with proprietary scientific datasets and knowledge bases
• Evaluating model customization strategies, including fine-tuning, and optimization techniques
• Architecting vector search and embeddings pipelines using modern retrieval technologies
• Implementing AI evaluation frameworks, monitoring, security controls, and governance practices for regulated scientific environments
• Integrating AI services with AWS cloud infrastructure and enterprise data platforms
• Establishing CI/CD workflows, automated testing, and scalable deployment strategies
• Collaborating closely with scientists, bioinformaticians, product teams, and engineers to identify high-impact AI opportunities
• Mentoring engineers and contributing to long-term AI platform strategy

Technologies:
LLMs, Agentic AI, RAG, Python, PyTorch, Vector, relational and non-relational databases, PostgreSQL, AWS, CI/CD, Cloud Architecture, AI Evaluation & Monitoring.

Driving the development of next-generation AI assistants that accelerate scientific discovery and operational efficiency across the organization.