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Tier-1 brand, mid-level ML role in metro with popular Data Scientist title increases applicant competition.
Requires scientific domain experience and life-sciences familiarity, limiting cross-industry transferability.
Explicit 5–9 years and specialized agentic AI, LLM, RAG, and scientific-model integration requirements increase shortlisting strictness.
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Design and build agentic AI systems integrating foundation and domain-specific models to accelerate scientific discovery across protein engineering, structure prediction, disease biology, and target identification.
Develop reusable AI components and workflows supporting multi-step reasoning, tool orchestration, knowledge retrieval, and human-in-the-loop decision making in scientific environments.
Collaborate closely with scientific domain experts and AI teams to translate research needs into scalable, enterprise-ready AI solutions and contribute to architectural and evaluation decisions.
Bachelor's or Master's degree in Computer Science, Engineering, Computational Biology, Bioinformatics, or a related field.
5 to 9 years of professional experience in AI and machine learning systems development with strong Python expertise.
Hands-on experience with Large Language Models, agent frameworks (e.g., LangChain), Retrieval-Augmented Generation (RAG), vector databases, and API-driven architectures.
Not explicitly mentioned in the JD: Notice period or location requirements.
Experienced in building production-quality AI/ML software systems focused on scientific computing workflows and knowledge-driven AI architectures.
Skilled at collaborating closely with cross-functional scientific teams and AI domain leads to bridge research and engineering for impactful AI solutions.
Capable of influencing AI architectural direction, evaluation frameworks, and driving adoption of reusable AI components in scientific and enterprise contexts.