Sr Associate Data Scientist

The GCF4 Senior ML Engineer – Agentic AI & Scientific Systems is a senior technical contributor responsible for designing, building, and integrating AI capabilities that accelerate scientific discovery across domains such as protein engineering, structure prediction, disease biology, and target identification.

This role focuses on developing agentic AI systems and scientific AI workflows that combine foundation models, domain-specific models, knowledge sources, and computational tools into reusable solutions that support scientific decision-making.

The engineer works closely with scientific domain leads to translate research needs into scalable AI solutions and reusable capabilities. This role serves as a bridge between scientific innovation and enterprise AI platforms, helping establish a foundation for next-generation AI-assisted scientific workflows.

Core Responsibilities

Agentic AI Systems Development

Design and implement agent-based systems that support complex scientific workflows.

Develop capabilities including:

Tool calling and tool orchestration

Multi-step reasoning workflows

Retrieval-augmented generation (RAG)

Knowledge-grounded AI systems

Human-in-the-loop decision workflows

Multi-agent collaboration patterns

Build reusable components for:

Agent orchestration

Context management

Memory and state handling

Workflow planning and execution

Scientific tool integration

Evaluate emerging agent frameworks and contribute to standards and best practices across projects.

Scientific AI & Model Integration

Integrate foundation models and scientific AI models into end-to-end workflows.

Examples may include:

Protein language models

Structure prediction models

Biological foundation models

Knowledge graph-based systems

Predictive machine learning models

Develop reusable APIs, services, and interfaces that allow AI agents and applications to consume scientific models and computational tools.

Collaborate with scientific domain experts to identify appropriate modeling approaches and evaluate solution effectiveness.

Knowledge Systems & Retrieval

Design and implement knowledge-driven AI systems that connect LLMs and agents with enterprise and scientific data.

Develop solutions utilizing:

Vector databases

Knowledge graphs

Graph-RAG architectures

Scientific literature and domain knowledge repositories

Ensure AI systems leverage authoritative knowledge sources and support traceability and explainability.

AI Workflow Engineering

Develop end-to-end workflows that combine:

Data ingestion and preparation

Knowledge retrieval

Model inference

Scientific analysis

Create reusable workflow patterns that can be applied across multiple scientific domains and projects.

Contribute to architectural decisions regarding workflow design, model integration, and AI system composition.

Evaluation & Responsible AI

Develop evaluation frameworks for AI systems, agents, and workflows.

Establish approaches for measuring:

Accuracy

Reliability

Scientific relevance

Hallucination rates

Workflow effectiveness

User adoption and impact

Support responsible AI practices including transparency, traceability, and governance requirements.

Collaboration & Scientific Partnership

Partner closely with:

AI domain leads

Scientists and researchers

Data engineering teams

Platform engineering teams

Enterprise AI platform teams

Translate scientific requirements into technical solutions and provide guidance on AI capabilities, limitations, and implementation approaches.

Contribute to technical design reviews and mentor junior team members where appropriate.

Core Competencies

Strong engineering background in AI and machine learning systems.

Hands-on experience with:

Large Language Models (LLMs)

Agent frameworks (LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or similar)

API-driven architectures

Python-based AI and ML ecosystems

Understanding of:

Machine learning lifecycle and evaluation

Scientific computing workflows

Distributed systems and scalable architectures

Knowledge graph concepts and graph-based AI approaches

Ability to operate effectively in highly collaborative, cross-functional scientific environments.

Core Success Measures

Delivery of reusable AI capabilities and agentic workflows

Adoption of AI solutions by scientific teams

Quality and reliability of deployed AI systems

Reduction of manual effort through workflow automation

Reusability of components across multiple scientific domains

Effective collaboration with scientific and engineering stakeholders

Key Relationships

Works closely with:

GCF6 Scientific AI Leads

Scientists and domain experts

Infrastructure and production engineering organizations

Decision Authority

Makes implementation decisions regarding:

Agent architectures

Workflow composition

Knowledge retrieval strategies

Model integration approaches

Evaluation methodologies

Influences broader architectural direction through technical expertise and collaboration with senior technical leaders.