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Tier-1 brand, common Data Scientist title, mid-level experience, and metro location increase competition.
Role requires scientific domain knowledge and specialized agentic AI expertise, limiting cross-industry transferability.
Multiple mandatory ML/AI, agentic systems, production engineering skills and explicit years create strict filters.
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Design, build, and integrate agentic AI systems and workflows that accelerate scientific discovery in domains like protein engineering, disease biology, and target identification.
Develop reusable AI components and end-to-end workflows combining foundation models, domain-specific models, knowledge sources, and computational tools for scientific decision-making.
Collaborate closely with scientific domain leads and enterprise AI platform teams to translate research needs into scalable, reusable AI solutions and support next-gen AI-assisted scientific workflows.
Bachelor's or Master's degree in Computer Science, Engineering, Computational Biology, Bioinformatics, or related field.
5-9 years of experience developing AI and machine learning solutions, with strong Python expertise and production-quality software design experience.
Hands-on experience with Large Language Models (LLMs), agent frameworks (e.g., LangChain, AutoGen), Retrieval-Augmented Generation (RAG), vector databases, and knowledge-driven AI architectures.
Experience working in scientific computing workflows and collaborating in cross-functional scientific environments.
Senior technical contributor familiar with agent-based AI system design and scientific AI model integration in biotechnology or related fields.
Experience operating at the intersection of AI development and scientific research, able to translate complex scientific problems into scalable AI workflows.
Ability to develop reusable, production-grade AI components and influence architectural decisions across scientific and engineering teams.