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This is a hands-on, early-career role for someone who is energised by working in a startup or scale-up environment: practical, curious about AI, comfortable with ambiguity, and able to move between technical work and working directly with the people on the ground.

Based on-site at RDM, you will work out where a tool genuinely helps, set it up in the real environment, train the people who use it, and handle first-line support so the tools stay in use. You will be the human bridge that turns the software we build into ways of working that stick.

The move to AI-augmented ways of working does not happen on its own across a workforce , it needs someone supporting it day to day. This role is that person.

Key Responsibilities

On-the-Ground Tool Adoption

• Act as the on-site point of contact for the AI tools, agentic systems, and internal software we deploy.

• Identify where a tool solves a real workflow problem on the ground, and where it does not.

• Set up and configure tools in the live environment so they work in the real-world context, not just in theory.

• Implement solutions from existing tools and services

Training and Support

• Train staff and support them until they can use the systems confidently on their own.

• Provide first-line support, diagnosing and resolving workflow and technical issues on-site without waiting on the build team.

• Keep people using the tools over time, so the investment we make in building them pays off.

Feedback to the Build Team

• Feed usage patterns and friction back to the Digital & Systems team to improve what we build.

• Help the team prioritise based on what actually helps people on-site.

What We’re Looking For

• Master’s graduate or early-career engineer (0–3 years’ experience).

• Hands-on with IT systems and software.

• Experience with, or strong interest in, AI and agentic augmentation.

• Python development.

• Strong problem-solving — able to diagnose workflow and technical issues on-site and find a working fix independently.

• Works well with non-technical users, and can translate between the build side and the people using the tools.

• Comfortable being based on-site at RDM and working directly alongside the people you support.

Nice to Have

• Experience deploying or supporting software in an industrial, manufacturing, or operational environment.

• Familiarity with agentic AI systems or LLM-based tooling.

• Experience in a startup or scale-up where roles are broad and priorities shift quickly.

• Interest in climate tech, energy, manufacturing, or deep tech.

The Person Who Will Thrive in This Role

You will likely succeed in this role if you are practical, proactive, and happy working at the pace of a startup. You enjoy being close to the people you help and get satisfaction from seeing a tool you set up actually get used. You do not need everything handed to you , you can walk onto the floor, work out what is getting in someone’s way, and fix it.

You are comfortable being the bridge between engineers and non-technical users, and you can explain technical things simply. You bring low ego, strong ownership, and genuine curiosity about how AI can change the way a workforce operates.

What success looks like after 12 months

After 12 months, the Forward Deployed Engineer will have become the trusted on-site face of the tools we deploy. Adoption of AI tools, agentic systems, and internal software at RDM will be visibly higher and more durable, because someone is there every day to set tools up, train people, and keep them running.

Crucially, the build team will no longer be the bottleneck for adoption. First-line support and onboarding will sit with the Forward Deployed Engineer rather than pulling senior engineers off build work, allowing the Digital & Systems team to ship more while usage keeps pace. A clear feedback loop will be in place, so what gets built is shaped by what genuinely helps people on the ground. After 12 months, Paebbl should feel that the time it invests in building tools reliably turns into tools that are actually used.