FDE

Compensation: £70,000 – £90,000 Base + Performance Related Bonus + Benefits

Role: Forward Deployed Engineer (FDE) – Senior AI

Location: UK-based, fast-growing AI-for-energy company; flexible remote/hybrid with regular client travel

Cloud Experience: Must have AWS or Azure (certifications desirable)

Management: No direct line management required

Consultancy/Energy Experience: Highly beneficial, not essential

Visa Sponsorship: Not currently available; right to work and UK residency required

Flexibility: Genuinely flexible on how, when, and where you work — we care about quality of delivery, not location

Diversity & Inclusion: Actively building a diverse team — we particularly encourage applications from women and underrepresented groups in technology and energy.

Who We Are

Hypercube is a rapidly growing AI-for-energy company building CAIRN, an operational intelligence platform that connects data, workflows, and decision-making across the energy asset lifecycle. We work with asset owners, operators, traders, and fund managers to replace fragmented tools and manual processes with a single intelligent layer across their portfolio. Our belief is simple: real impact in a complex sector requires deep understanding, not just good software. We put our best engineers directly alongside clients, embedded in their world, solving real operational problems, and what we learn in the field continuously shapes the platform.

Role Purpose

Most AI engineers build for platforms. You'll build for decisions, real ones, at real energy companies, at the point where AI meets actual megawatts. As one of Hypercube's first Forward Deployed Engineers, you'll help build the function from the ground up, working alongside the team to shape how we deploy, how we engage clients, and how we grow. You'll embed with clients, own the technical relationship, and make CAIRN work where the data is real, the stakes are high, and the requirements were never written down. You'll run discovery sessions with operations directors and trading desks, deploy AI workflows that change how energy assets are managed, and stay with the client until the system is running, trusted, and delivering measurable value. This is a role for someone who is energised by the complexity of real energy operations, thrives when navigating uncertainty and shaping solutions from the ground up, and is just as confident presenting to a CFO as they are debugging a data pipeline. Strong communication with clients and internally with the platform and engineering teams is as important here as technical ability. The evidence you bring back from the field is what drives the CAIRN roadmap forward.

Key Responsibilities

• Client deployment & delivery
• Own end-to-end AI deployments at assigned accounts, from scoping through to production and ongoing operation
• Configure CAIRN's AI modules to each client's operational context, data landscape, and workflow requirements
• Build a deep working knowledge of CAIRN, its architecture, modules, and limits, so you can configure and extend it with confidence and communicate its capabilities honestly with clients
• Work with data pipelines and APIs to connect client data sources to CAIRN's AI layer
• Build human-in-the-loop controls into every deployment, ensuring that where AI informs high-stakes operational decisions, the right people remain accountable for the outcome
• Define measurable success metrics with clients from day one and track them throughout delivery
• Understand the commercial context of each account, what success is worth to the client, what is in scope, and what a strong ROI case looks like, and keep that front of mind throughout delivery

• Stakeholder engagement & communication
• Lead structured discovery sessions with stakeholders at all levels, from operations engineers to C-suite
• Engage confidently in technical and commercial conversations with energy sector experts and build credibility through genuine sector depth and strong relationships
• Run client playbacks and roadmap sessions that translate technical progress into business outcomes
• Build trusted relationships and internal champions who drive adoption
• Handle difficult conversations about scope, timelines, and platform limitations

• Quality & standards
• Write production-grade code that is readable, tested, documented, and maintainable by others
• Hold every client-facing output, code, documentation, presentations, and workshops, to the same high standard
• Make architecture decisions that balance quality, safety, latency, cost, and model risk, and communicate those tradeoffs clearly to clients
• Contribute to the FDE playbook: configuration patterns, integration templates, and AI governance frameworks

• Collaboration & platform contribution
• Mentor and collaborate with other FDEs, sharing field learnings, deployment patterns, and hard-won experience as the practice grows
• Work closely with the platform and engineering teams, sharing field evidence that drives the CAIRN roadmap forward
• Raise feature requests and bug reports with the client context needed to prioritise them

Technical Skills & Experience

We value potential alongside proven experience. Apply even if you meet only some criteria.

Core skills

• Agentic AI & LLMs: Production experience deploying large language models and agent-based workflows, not just notebooks
• Agent orchestration frameworks: Hands-on experience with LangGraph, LangChain, LlamaIndex, or CrewAI building multi-step agentic systems
• RAG & knowledge systems: Practical experience designing and building retrieval-augmented generation pipelines
• AI evals and observability: Experience building evaluation frameworks and using tools such as LangSmith to monitor and constrain agent behaviour in production
• LLMOps: Familiarity with the operational practices for deploying, monitoring, and maintaining LLMs in production environments
• Data science and model development: Ability to build, evaluate, and deploy bespoke predictive models where off-the-shelf AI approaches are insufficient, with practical experience in model training, feature engineering, and evaluation
• Python: Clean, production-quality code under real-world constraints, incomplete data, unreliable APIs, shifting requirements
• Cloud AI (AWS/Azure): Deploying AI solutions in production, including monitoring and reliability
• Data pipelines: Comfortable building and maintaining ETL/ELT pipelines connecting messy source systems to AI applications
• SQL: Solid querying and data modelling skills
• AI model management: MLFlow, Hugging Face, or equivalent

Nice to have

• Experience with time-series modelling, anomaly detection, or forecasting in operational or industrial contexts
• Infrastructure as Code (Terraform or equivalent)
• Containers: Docker, Kubernetes
• Streaming: Kafka, Kinesis
• Relevant certifications (AWS, Azure, Anthropic, Databricks, or equivalent)

Desirable experience

• Energy sector background, whether in engineering, analysis, consulting, or an operational role
• Client-facing or consulting experience alongside technical delivery
• A track record of building in messy, real-world environments and making things work reliably
• Public engagement through blogging, speaking, or open-source contribution

What's in it for you?

• Build the function. You are one of Hypercube's first FDEs. What you help shape becomes the playbook everyone else follows
• Field ownership. You own your deployments end-to-end, the approach, the architecture, the relationships, and the outcome
• Direct platform influence. The evidence you bring from the field shapes what CAIRN becomes
• Work that matters. The energy transition needs better decisions. You'll be building the tools that make them possible
• Career development. A clear path to Principal FDE and beyond, with transparent progression criteria and active mentorship
• Personal branding. Support developing your public profile, speaking, writing, and representing Hypercube in the energy AI conversation
• Genuine flexibility. We are genuinely flexible on how, when, and where you work. We care about the quality of what you deliver, not where you deliver it from

Benefits

• Enhanced pension
• Performance related bonus
• Enhanced maternity/paternity
• Peer cash award scheme
• Cycle to work scheme
• Flexible remote/hybrid working (with regular client travel)
• Events and community participation
• Private health insurance
• Health cash plan
• EV leasing scheme
• Training and events budget (£2,000/yr)
• Mentorship programmes

Diversity & Inclusion

We are actively working to build a team that reflects the diversity of the sector we want to transform. We particularly encourage applications from women and underrepresented groups in technology and energy, and we are committed to a fair, structured hiring process for every candidate.

Ready to Apply?

If this role excites you, apply via our careers page or contact us directly, even if you meet only some criteria. We want engineers who are energised by client impact, not just platform depth. If you are considering whether to apply, we would rather you did — diverse teams build better things.

N.B. We are currently not able to sponsor visas.