AI Engineer - AI Drug Discovery Company

Few&Far; Bradley Stoke; On-site; GBP 54,000 - 90,000; Full time

Job summary

Few&Far is seeking an Agentic AI Engineer for its AI drug discovery initiative in the UK, with remote work and occasional Cambridge office visits. You will advance agentic workflows to help scientists generate testable hypotheses and improve evidence-based reasoning across platforms.

You should have 2+ years of software/ML engineering experience, strong Python skills, and fluency with modern LLMs, agents, tools, and evals.

Qualifications

• Shipped LLM-agentic systems that deliver real production value.
• 2+ years of software/ML engineering experience with Python.
• Fluent with modern LLM and agent toolkits, APIs, prompting and evals.

Responsibilities

• Build and refine agentic workflows to generate testable hypotheses for experiments.
• Translate discovery problems into agent designs and assess value of agentic approaches.
• Integrate agents with internal knowledge sources for evidence-based reasoning.
• Expand and maintain GenAI tooling as needs evolve.

Skills

Python

ML engineering

Agentic workflows

LLM tooling

Model APIs

Prompting

Tool use

RAG

Agent frameworks

Evaluation harnesses

Job description

Agentic AI Engineer — AI Drug Discovery Company (UK, Rare Disease Drug Discovery)

Up to £90k + Equity

Remote (Occasional visits to Cambridge Office)

We are working with an AI company applying artificial intelligence to drug discovery, with a focus on rare diseases that currently have no approved treatment. The company combines proprietary data, AI and deep scientific expertise to identify promising treatments faster and more cheaply than traditional drug discovery approaches.

This is not a green-field build. There is an existing agentic framework and set of workflows already delivering impact across the platform, and you will work alongside the engineer who owns that infrastructure, with scope to take ownership of specific workflows and grow your influence over time.

Key responsibilities include:

Building and refining agentic workflows that help scientists generate the best testable hypotheses to progress.

Translating discovery problems into agent designs, and being clear-eyed about where an agentic approach adds value and where it does not.

Integrating agents with internal knowledge sources so they reason over the right evidence for each problem.

Expanding and maintaining existing GenAI tooling as the team's needs and the wider ecosystem evolve.

What we are looking for:

You have shipped LLM-agentic systems that deliver real value in production, not just prototypes, with tools, multi-step orchestration and reliable behaviour.

You have at least two years of software engineering or ML engineering experience, with strong fundamentals and clean, tested, maintainable Python.

You are fluent in the modern LLM and agent toolkit, including model APIs, prompting, tool use, RAG, MCP, agent frameworks and evals.

You enjoy working closely with non-engineers and are comfortable sitting with a scientist to understand what they actually need.

Experience in drug discovery, biology or another life science domain is a bonus, as is familiarity with knowledge graphs, experience building evaluation harnesses for LLM systems, or a track record of picking up unfamiliar domains quickly.