Role Description
Senior Forward-Deployed Engineer – AI / LLMs - Early stage Start-up
📍 London, with travel to customers weekly as required.
💼 Full-time, permanent role
We’re supporting an AI Infrastructure start-up that helps organisations turn their own data into AI models and deploy them in their chosen environment.
As customers move into production, the team is hiring a Senior Forward-Deployed Engineer to own the technical journey from initial discovery through to deployment and ongoing adoption.
This is a hands-on engineering role with significant customer interaction. You’ll work alongside sales and engineering, building tailored demonstrations, validating model performance and helping customers get real workloads into production.
What we’re looking for:
- Strong hands-on experience with open-weight LLM post-training, including fine-tuning techniques such as LoRA and supervised fine-tuning.
- Experience designing evaluations and balancing model quality, latency and inference costs.
- A track record of shipping models into production for real users.
- Strong Python skills, with experience across GPU infrastructure, containers and distributed workloads using tools such as Ray or Kubernetes.
- Confidence leading technical conversations with CTOs and explaining trade-offs to commercial stakeholders.
- A background in forward-deployed, solutions or applied engineering, or a related role combining technical delivery with customer engagement.
- Based in London and able to travel to customers as needed.
Useful additional experience:
- Financial services or other regulated customer environments.
- Data residency and regulatory requirements.
- Working across multiple GPU providers and sizing workloads to hardware.
- Early-stage startups.
About Forward Deployed Engineering
Forward Deployed Engineers are embedded directly with customers to build custom solutions, integrate products into existing infrastructure, and bridge the gap between product engineering and customer success. The role combines deep technical skills with the ability to operate in client environments and translate business requirements into working software.
Originally pioneered by Palantir, the FDE model has spread across AI, enterprise SaaS, and cloud infrastructure companies. FDEs write production code, architect integrations, train customer teams, and feed product insights back to the core engineering organization. At companies like OpenAI, Salesforce, and Databricks, FDE teams are treated as elite engineering units that can ship custom solutions in days rather than quarters.
Typical FDE stack: Python, TypeScript, SQL, REST/GraphQL APIs, cloud platforms (AWS/GCP/Azure), and increasingly LLM APIs and AI orchestration frameworks. Strong communication and the ability to context-switch between technical and business conversations are as important as coding ability.
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