Agentic AI Engineer: Shape AI-Driven Drug Discovery

Healx; Cambridge; Hybrid; GBP 90,000 - 120,000; Full time

Benefits offered by this job

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Pension 7%

Life insurance 4x base

Private medical insurance

25 days annual leave

Hybrid working

Family leave policies

Learning & development budget

Volunteer day

Job summary

Healx in Cambridge (UK) is hiring an Agentic AI Engineer to build LLM-agentic workflows that power our drug discovery platform. You will design and refine agents that reason over our knowledge graph, literature and proprietary methods to generate testable hypotheses for rare diseases.

You’ll join a cross-functional team of ML engineers, software engineers, bioinformaticians and scientists, collaborating closely with non-engineers to turn scientific questions into practical agent designs and

Qualifications

• Built and shipped LLM-agentic systems delivering real value.
• At least 2 years of software/ML engineer experience.
• Fluency with modern LLM/agent toolkits for production use.
• Ability to work with scientists and non-engineers to understand needs.

Responsibilities

• Build and refine AI-agentic workflows on our internal framework.
• Translate discovery problems into agent designs and evaluate value.
• Integrate agents with knowledge graph and literature sources.
• Expand GenAI tools to meet evolving team needs.
• Contribute to evaluation, testing and quality standards.
• Write clear, maintainable, well-documented code.

Skills

LLM-agentic systems

Python programming

Knowledge graph integration

AgentSpace usage

Tools

ADK

AgentSpace

Job description

Healx in Cambridge (UK) is hiring an Agentic AI Engineer to build LLM-agentic workflows that power our drug discovery platform. You will design and refine agents that reason over our knowledge graph, literature and proprietary methods to generate testable hypotheses for rare diseases.

You’ll join a cross-functional team of ML engineers, software engineers, bioinformaticians and scientists, collaborating closely with non-engineers to turn scientific questions into practical agent designs and