Agentic AI Lead – Protein Design & Molecular Engineering

Amgen

India - HyderabadPosted about 1 month agoDiscoveredMatch locked

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Research Job Description

Position Overview

The GCF6 Agentic AI Lead – Protein Design & Molecular Engineering is a senior scientific and technical leader responsible for defining and driving AI-enabled workflows that accelerate protein engineering, structure prediction, molecular design, and related discovery activities. This role combines deep domain expertise in computational biology and molecular engineering with a strong understanding of emerging AI technologies, including foundation models, scientific AI, and agentic systems. The leader identifies high-value scientific opportunities, designs AI-assisted workflows, and partners with ML engineers to build reusable agentic capabilities that enhance scientific productivity and decision-making. This role serves as the primary scientific lead for AI applications in protein engineering and molecular design.

Scientific AI Strategy

Develop and maintain a roadmap for AI-enabled capabilities supporting:

Protein engineering
Structure prediction
Protein design
Motif discovery
Protein-ligand interactions
Sequence-function analysis
Molecular optimization

Identify opportunities where AI agents, scientific models, and automation can significantly improve scientific workflows and outcomes.

Agentic Workflow Design

Design AI-assisted workflows that combine:

Scientific reasoning
Foundation models
Protein language models
Structure prediction systems
Computational biology tools
Internal and external knowledge sources

Define agent responsibilities, decision pathways, tool integration patterns, and human oversight requirements. Guide development of multi-agent systems that support complex scientific analyses and discovery workflows.

Scientific Leadership

Serve as the primary interface with research scientists and computational biology teams. Translate scientific challenges into AI opportunities and technical requirements. Provide scientific oversight for AI-enabled solutions and ensure outputs align with biological principles and research objectives.

Scientific Model Integration

Guide adoption and evaluation of scientific AI technologies including:

Protein language models
Structure prediction models
Generative protein design approaches
Molecular foundation models
Emerging computational biology platforms

Assess scientific utility, limitations, and opportunities for integration into broader workflows.

Collaboration & Delivery

Partner closely with:

ML engineers
Data engineering teams
Platform teams
Research scientists
External collaborators

Drive prioritization and execution of AI initiatives within the protein engineering and molecular design portfolio.

Core Competencies

Deep expertise in one or more of:

Computational biology
Protein engineering
Structural biology
Molecular modeling
Protein design

Strong understanding of:

Foundation models
Scientific AI
Agentic AI systems
Scientific workflow automation

Ability to connect scientific objectives with AI capabilities and practical implementation strategies.

Core Success Measures

Adoption of AI-enabled workflows by scientific teams
Scientific impact of deployed solutions
Reusability of agentic capabilities across programs
Acceleration of scientific discovery workflows
Effective collaboration across research and engineering organizations

Preferred Qualifications

PhD in Computational Biology, Bioinformatics, Structural Biology, Biophysics, Protein Engineering, Computer Science, or related field. Experience applying AI and machine learning to molecular or biological discovery problems. Demonstrated leadership in cross-functional scientific initiatives. .

Not included in the source posting: about the role, what you'll do, benefits.

Skills

artificial-intelligencemachine-learning

Who can apply

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