Forward Deployed Engineer

Mint Selection; Greater London; Hybrid; GBP 90,000 - 120,000; Full time

Job summary

Mint Selection is helping forward deploy a new generation of AI-enabled software in London. The Forward Deployed Engineer will work directly with business teams to move from discovery through live deployment and measurable impact.

You will prototype with real data, build production code in Python and APIs, and help define evaluation criteria. The role blends hands-on engineering with product thinking in a hybrid London setting.

Job description

Forward Deployed Engineer

London - Hybrid

Two founding appointments for product-minded software and AI engineers who want to work directly with business teams and own solutions from discovery through live deployment and measurable impact.

The company and the opportunity

• Our client develops, finances, builds, owns and operates the flexible infrastructure needed to balance a renewables-led power system. Its portfolio spans battery storage, flexible generation, pumped storage hydro and green hydrogen. Backed by a major PE firm, the business has grown from a developer into an integrated operating platform with a substantial UK portfolio and European growth ambitions.
• Our client is establishing an Applied AI capability to improve how work gets done across the asset lifecycle, from development and construction through operations, optimisation and trading. The approach is to solve valuable business problems, deploy working products and build shared capabilities where those products demonstrate a need.

The role

• A founding engineering role with direct access to domain teams, real infrastructure problems and the scope to see your work used. You will help establish how AI is built and deployed across an asset owner, with room to shape both the products and the team as the business grows.
• The remit is business-wide, working with teams across Development, Project Delivery, Investment, Asset Management and Trading. Priorities will be agreed according to business need and potential impact. You will investigate real workflows, understand the underlying data and systems, and decide whether AI, conventional software, automation or a change in process is the right answer.
• Working with the incoming AI leader and domain teams, you will move quickly from an ambiguous problem to a tested prototype, then take successful solutions into production. Product discovery and value mapping will initially be supported by the AI leader; a dedicated AI Product Manager or Deployment Strategist may complement the team as it grows.

Building the founding capability

• Alongside individual products, the two founding engineers will establish the first reusable AI engineering capabilities: model access and serving, deployment, evaluation, observability, CI/CD and production patterns. These should emerge from delivered products and be reused where they help, rather than becoming a large central platform programme upfront.
• You will work with Data Engineering and technology partners on data access, provenance, identity and shared infrastructure, with clear ownership of live service reliability. You will contribute to architecture and code reviews, use AI-assisted development thoughtfully and help shape the engineering standards of the growing team.

What you will own

• Work directly with users to understand target outcomes, investigate systems and information flows, and turn business problems into practical solution designs.
• Prototype with real data, test technical hypotheses with users and define evaluations before committing to production architecture.
• Build and operate software across Python, APIs, data integration and lightweight frontends, incorporating LLMs, retrieval, tool use and agent workflows where useful.
• Own deployment and early production operation, including testing, security, permissions, monitoring and responses to failure.

The person

• Strong software-engineering fundamentals and evidence of designing, building and operating production software. Python capability is important, alongside the range to work across a product.
• Practical experience applying LLMs, retrieval, tool use or evaluation. We will also consider exceptional product-focused full-stack engineers with credible applied AI work and the ability to develop quickly.
• Sound product judgement and the ability to work directly with technical and non-technical users, ask useful questions and make sensible trade-offs.
• Comfort moving between fast experiments and maintainable production code, with a pragmatic view of build versus buy.
• High ownership, curiosity and the drive to solve difficult problems. Energy experience and a PhD are not requirements; evidence of what you have built matters more than a particular title or employer.

What success looks like

• Useful products are live and used by their intended business teams. Their impact is measured against agreed business baselines, alongside adoption, reliability and AI quality. Successful solutions improve through user feedback; weak ideas are stopped or redesigned. Shared engineering patterns make each subsequent deployment easier without compromising accountability for production stability.