Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

Tailor your resume to this AI Enablement Engineer role.

Rezi rewrites your resume against Foundry's job description. Free.

Resume score gauge reading 58 out of 100

Don't guess if your resume is good enough.

See how it scores against the AI Enablement Engineer posting at Foundry — free, in seconds.

About the Role

This role is for an experienced AI practitioner to design, deploy, and maintain the infrastructure, platforms, and best practices for AI adoption across a 300-person company. The focus is on enabling all teams, including citizen AI users, to leverage AI safely and efficiently, moving solutions from experiment to production.

Responsibilities

Enable and accelerate AI adoption across the company, removing barriers and providing necessary infrastructure, tooling, and support.
Design, deploy, and maintain 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.
Build, deploy, and operate AI agents and agentic workflows from prototype to production.
Evaluate new models, tools, and vendors, providing evidence-based recommendations.
Share experience through documentation, workshops, demos, and hands-on support.
Deploy and manage AI infrastructure in the cloud, focusing on reliability, scalability, and cost efficiency.
Optimize and tune model serving for available hardware, including self-hosted inference.
Build and maintain data pipelines and contribute to data lake and lakehouse architecture.
Apply software engineering best practices to AI systems, including infrastructure as code, CI/CD, automation, monitoring, and documentation.
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 by providing guardrails and paved paths.
Collaborate with GRC and Legal teams to translate AI policy and guidance into 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 to understand needs and deliver AI infrastructure and capabilities.
Support and coach citizen AI users, helping them build solutions within sensible boundaries.
Contribute to AI awareness, training, and safe-use guidance across the company.

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 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 modeling.
A security-minded approach, with the judgment 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 served.

Skills

AI Engineering
ML Engineering
Platform Engineering
AI agents
LLM-based applications
Cloud platforms
Cloud-native tooling
GCP
AWS
Python
CI/CD
Automation
Infrastructure as code
Version control
Data engineering
ELT processes
Data lakes
Data modeling
Security
Communication
Prompt design
Evaluation
Observability
Cost management
Model serving
Self-hosted inference
vLLM
Data pipelines
Lakehouse architecture
Retrieval
Analytics
Access control
Secrets management
Data handling
Logging
GRC
Legal
Data leakage mitigation
Prompt injection mitigation
Over-reliance mitigation
Shadow AI mitigation
IP exposure mitigation
LiteLLM
LLM gateway
LLM proxy tooling
BigQuery
BigLake
Dataplex
Dataflow
MLOps
LLMOps
Evaluation frameworks
Model lifecycle management
Citizen developer support
Low-code tooling
Media and Entertainment industry
VFX industry
Creative software industry

Location

London, UK

Work Type

Hybrid

Experience Level

Experienced

Benefits

25 days holiday + bank holidays
Pension scheme & Life Assurance
Health cash plan & Medical Insurance
Season ticket loan
Company’s social events
Personal Annual Development Time
Passion Days
Anniversary Day off