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Strong employer brand plus metro location, but specialized ML/agent skills moderate competition.
Role requires domain-specific scientific AI experience, making backgrounds less transferable across industries.
Explicit 5–9 years and specialized LLM/agentic AI, RAG, and scientific domain skills enforce strict filters.
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Design, build, and integrate AI capabilities accelerating scientific discovery across domains such as protein engineering, structure prediction, and disease biology.
Develop agentic AI systems combining foundation and domain-specific models, knowledge sources, and computational tools into reusable scientific decision-making workflows.
Collaborate closely with scientific domain leads and AI/platform teams to translate research needs into scalable, reusable AI solutions and establish next-gen AI-assisted workflows.
Work Experience Required: 5–9 years (Preferred Bachelor's degree with experience).
Bachelor's or Master's degree in Computer Science, Engineering, Computational Biology, Bioinformatics, or related field.
Strong hands-on experience in AI and machine learning solution development, expertise in Python and modern AI/ML frameworks.
Experience with Large Language Models, agent frameworks (e.g., LangChain, LangGraph), Retrieval-Augmented Generation, vector databases, and API-driven architectures.
Senior technical contributor comfortable bridging scientific research and enterprise AI platforms with strong engineering skills in AI/ML systems engineering.
Proven ability in designing and delivering reusable AI capabilities and workflows adopted by scientific teams across multiple scientific domains.
Experienced working cross-functionally with scientists, researchers, data engineering, and platform teams in collaborative scientific environments.