AI Engineer

Foundry; Greater London; Hybrid; GBP 120,000 - 170,000; Full time

Job summary

Foundry is seeking an experienced AI practitioner to design, deploy and maintain infrastructure, platforms and best practices for AI adoption across the business. You will enable engineering teams and citizen AI users, balancing risk and speed, with a focus on production-ready AI capabilities at scale.

Two days per week in our London Soho office, joining a pragmatic, hands-on team within Corporate Technology and AI.

Qualifications

• Strong background as an AI/ML engineer, with track record of moving AI from prototype to production.
• Hands-on experience building and operating AI agents and LLM-based applications in production with an understanding of risks.
• Strong experience with cloud platforms and cloud-native tooling (GCP or AWS).
• Strong proficiency in Python.
• Strong experience with CI/CD and automation, including infrastructure as code and version control.
• Practical experience with data engineering, including ELT, data lakes and data modelling.
• Security-minded approach balancing risk and velocity.

Responsibilities

• Enable and accelerate AI adoption across the company, focusing on Engineering by removing barriers and providing infrastructure and tooling.
• Design, deploy and maintain the supporting technology for AI use, including model access, gateways, agent frameworks, tooling and shared services.
• Establish best practices and reference architectures for building with AI, including prompt design, observability and cost management.
• Build, deploy and operate AI agents and agentic workflows from prototype to production.
• Evaluate new models, tools and vendors with recommendations.
• Share experiences through documentation, workshops, demos and hands-on support.
• Deploy and manage AI infrastructure in the cloud with focus on reliability and cost efficiency.
• Optimize model serving for hardware and self-hosted inference (e.g., vLLM).
• Develop data pipelines and contribute to lakehouse architectures for retrieval, analytics and evaluation.
• Apply software engineering best practices (IaC, CI/CD, automation, monitoring, documentation).
• Design AI platforms with security by default, including access control and logging.

Skills

AI Engineering

Production AI

Python

CI/CD

Tools

GCP

AWS

vLLM

LiteLLM

Job description

This is an opportunity for an experienced, hands-on AI practitioner to help shape how Foundry enables AI across the whole company. Sitting within the IT department and reporting to the Senior Director of Corporate Technology and AI, you will design, deploy and maintain the infrastructure, platforms and best practices that allow every team to adopt AI and accelerate with it. Engineering will be a major focus, but you will deliver capabilities for teams across the business, including citizen AI users, balancing risk and reward so that people can move quickly and safely. We are looking for someone who has already taken AI from experiment to dependable production use, learned from the mistakes, and can help us accelerate rather than rediscover the same lessons. Our scale is that of a 300-person company, so we value pragmatic, right-sized solutions over hyper-scale complexity. You'll spend a minimum of two days each week in our London office, in Soho.

Job Responsibilities

AI enablement and platform

• Enable and accelerate AI adoption across the company, with a particular focus on Engineering, by removing barriers and providing the infrastructure, tooling and support teams need.
• Design, deploy and maintain the supporting technology for AI use, including model access, gateways, agent frameworks, developer tooling and shared services.
• Establish and promote best practices, reference architectures and reusable patterns for building with AI, including prompt design, evaluation, observability and cost management.
• Build, deploy and operate AI agents and agentic workflows, from prototype through to reliable production use.
• Evaluate new models, tools and vendors, providing clear, evidence-based recommendations on what to adopt, what to wait on and what to avoid.
• Share your experience of what works (and what doesn't) through documentation, workshops, demos and hands‑on support.

Infrastructure, data and performance

• Deploy and manage AI infrastructure in the cloud, with a focus on reliability, scalability and cost efficiency.
• Optimise and tune model serving for available hardware, including self-hosted inference using tools such as vLLM.
• Build and maintain data pipelines and contribute to data lake and lakehouse architecture, to support AI use cases such as retrieval, analytics and evaluation.
• Apply software engineering good practice, including infrastructure as code, CI/CD and automation, monitoring and documentation, to AI systems.

Security, risk and governance

• Design AI platforms and workflows with security by default, including access control, secrets management, data handling and logging.
• Enable developers and citizen AI users to work safely, providing guardrails and paved paths rather than blockers.
• Work closely with our GRC and Legal teams to translate AI policy, approved-tools lists and data guidance into practical technical controls.
• Help assess and mitigate AI-specific risks such as data leakage, prompt injection, over‑reliance, shadow AI and IP exposure.
• Partner with teams across the business, including Engineering, Product, Security, Legal and Finance, to understand needs and deliver AI infrastructure and capabilities that make a measurable difference.
• Support and coach citizen AI users, helping them build useful solutions within sensible boundaries.
• Contribute to AI awareness, training and safe‑use guidance across the company.

Job Requirements

• Strong experience in an AI Engineering, ML Engineering, platform engineering or similar role, with a track record of taking AI solutions from prototype to production and operating them reliably.
• Hands‑on experience building and operating AI agents and LLM‑based applications in production, with an honest understanding of their risks and limitations.
• Strong experience with cloud platforms and cloud‑native tooling (e.g., GCP or AWS).
• Strong proficiency in Python.
• Strong experience with CI/CD and automation, including infrastructure as code and version control.
• Practical experience with data engineering, including ELT processes, data lakes and data modelling.
• A security‑minded approach, with the judgement to balance risk and velocity and to enable others safely.
• Strong communication skills, including the ability to explain technical topics to non‑specialists and build relationships across the business.
• A pragmatic, proactive and inquisitive mindset, able to work autonomously and set direction in a fast‑moving area.
• A genuine interest in technology, AI and the industry we serve.
• Experience tuning and serving local models on bare metal using tools such as vLLM, including GPU optimisation.
• Experience with LiteLLM or similar LLM gateway and proxy tooling.
• Experience with GCP data and lakehouse tooling (e.g., BigQuery, BigLake, Dataplex, Dataflow).
• Experience with MLOps and LLMOps practices, including evaluation frameworks, observability and model lifecycle management.
• Experience supporting non‑technical or citizen developer communities with AI and low‑code tooling.
• Experience in the Media and Entertainment, VFX or creative software industries.