Hybrid Agentic AI Engineer for Rare-Disease Drug Discovery

Healx; Cambridge; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

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7% employer pension contributions

Life insurance 4x base salary

Private medical insurance

25 days annual leave

Job summary

Healx in Cambridge is seeking an Agentic AI Engineer to design and build LLM-agentic workflows that power our drug discovery platform. You’ll work with scientists to translate questions into agent designs and integrate them with our knowledge graph and literature sources.

You’ll join a cross-functional team of ML engineers, software engineers and bioinformaticians, contributing to production-ready tools and rigorous evaluation. Hybrid working and career growth supported.

Qualifications

• Built and shipped LLM-agentic systems delivering real value.
• At least 2 years of software or ML engineering experience using Python.
• Fluency with modern LLM/agent toolkits, prompting, and evals.
• Experience collaborating with scientists to understand needs.
• Biotech/drug-discovery experience is a bonus.

Responsibilities

• Build and refine AI-agentic workflows on our internal framework.
• Translate discovery problems into agent designs and assess value.
• Integrate agents with knowledge graph and literature to reason over evidence.
• Expand and maintain GenAI tools to pace evolving needs.
• Develop evaluation and quality standards for agentic workflows.
• Write clear, maintainable code.

Skills

LLM-agentic systems

Python

Software engineering

Machine Learning

Knowledge graphs

Biotech domain experience

Tools

AgentSpace

ADK

Job description

Healx in Cambridge is seeking an Agentic AI Engineer to design and build LLM-agentic workflows that power our drug discovery platform. You’ll work with scientists to translate questions into agent designs and integrate them with our knowledge graph and literature sources.

You’ll join a cross-functional team of ML engineers, software engineers and bioinformaticians, contributing to production-ready tools and rigorous evaluation. Hybrid working and career growth supported.