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Strong employer brand, popular Data Scientist title, mid-level experience band, and metro location increase applicant competition.
Role requires scientific domain integration and life-sciences familiarity, making background transferability limited.
Specific agentic AI, LLM, RAG, vector DB, and production integration requirements make shortlisting highly strict.
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Design, build, and integrate agentic AI systems and scientific AI workflows to accelerate discovery in protein engineering, structure prediction, disease biology, and target identification.
Develop reusable AI components and workflows combining foundation models, domain-specific models, knowledge bases, and computational tools for scientific decision-making.
Collaborate with scientific domain leads and AI platform teams to translate research needs into scalable, traceable, and reliable AI solutions across multiple scientific domains.
Bachelor's or Master's degree in Computer Science, Engineering, Computational Biology, Bioinformatics, or related field.
5 to 9 years of experience developing AI and machine learning solutions; experience in production-quality software system design.
Strong hands-on proficiency with Python, AI/ML frameworks, Large Language Models (LLMs), agent frameworks (e.g., LangChain, AutoGen), and Retrieval-Augmented Generation (RAG).
Work Experience Required: 5–9 years; Notice period: Not explicitly mentioned in the JD.
Experienced in engineering reusable AI/agent workflows that integrate scientific and foundation models addressing complex scientific problems.
Capable of working closely with cross-functional teams including scientists, domain experts, platform engineering, and data engineering.
Strong technical leadership in architectural decisions for AI workflow design, model integration, agent orchestration, and responsible AI evaluation practices.