FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates deep expertise in MLOps, AI governance, and production machine learning platforms, with a strong focus on building scalable AI solutions and ensuring compliance in regulated environments. Proven ability to lead technical strategy, mentor teams, and operationalize AI innovations effectively.
Highest-signal resume keywords
MLOps ExpertiseLarge Language Models (LLMs)Python ProgrammingCloud-Native Services on AWSAI Governance and Compliance
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine Learning SolutionsModel DeploymentPrompt EngineeringEvaluation FrameworksDistributed Systems DesignProduction ServicesInfrastructure as CodeCI/CD PracticesAgentic AI WorkflowsRetrieval-Augmented Generation (RAG)
Soft Skills
Excellent CommunicationStakeholder Management
Tools & Technologies
KubernetesContainersAI Observability ToolsLakehouse Data PlatformsData Engineering Ecosystems
Industry Keywords
EnergyUtilitiesFinancial ServicesHealthcare
Tech Stack
Tools & technologiesAWSCloudCyber SecurityDistributed SystemsKubernetesPython
About the role
Key responsibilities & impact- Lead the architecture, technical strategy and roadmap for ENSEK's AI/ML Platform
- Design and build production-ready golden paths for Machine Learning, GenAI and Agentic AI solutions
- Establish standards for model lifecycle management, prompt versioning, deployment, serving, RAG, orchestration and secure tool access
- Build MLOps and LLMOps capabilities, including evaluation frameworks, experimentation tooling, observability, monitoring and cost management
- Create and maintain registries for AI products, models and prompts
- Partner with Data Scientists to operationalise machine learning models, GenAI applications and agentic workflows into reliable production services
- Define patterns for human-in-the-loop workflows, confidence-based routing and AI safety mechanisms
- Embed security, governance, compliance and responsible AI practices into the platform
- Collaborate with Legal, Cyber Security, Risk and Product teams to ensure AI solutions are safe, explainable and auditable in regulated environments
- Mentor ML Engineers and developers across the organisation
- Help establish engineering standards, best practices and the AI Engineering operating model
- Build the team, culture and operating model supporting AI innovation across ENSEK
- Enable product teams to build, deploy and operate AI solutions at scale through approved patterns and tooling
Requirements
What you’ll need- Significant experience as an AI Engineer, ML Engineer, Software Engineer or Technical Lead delivering production Machine Learning and AI solutions
- Experience operating at Staff Engineer, Principal Engineer or equivalent technical leadership level
- Deep expertise in MLOps and production machine learning platforms, including model deployment, serving, monitoring, evaluation and lifecycle management
- Proven experience building and operating applications powered by Large Language Models (LLMs)
- Strong hands-on experience with Retrieval-Augmented Generation (RAG), prompt engineering, evaluation frameworks and agentic AI workflows
- Excellent software engineering skills, ideally in Python
- Experience designing distributed systems and production services
- Experience building and operating cloud-native services on AWS using containers, Kubernetes, Infrastructure as Code and CI/CD practices
- Strong understanding of platform engineering principles, developer experience and self-service platform design
- Experience implementing AI governance, model safety, privacy controls and responsible AI practices
- Ability to make pragmatic technical decisions balancing performance, cost, scalability and risk
- Excellent communication and stakeholder management skills, explaining technical concepts and trade-offs to technical and non-technical audiences
- Experience with MCP (Model Context Protocol) or similar emerging standards is nice to have
- Experience building enterprise AI platforms supporting multiple product teams is nice to have
- Experience with lakehouse data platforms and data engineering ecosystems is nice to have
- Knowledge of regulated industries such as Energy, Utilities, Financial Services or Healthcare is nice to have
- Experience working with Agentic AI architectures and tool-using AI systems is nice to have
- Familiarity with emerging AI observability, evaluation and governance tooling is nice to have
Benefits
Comp & perks- 25 days’ holiday + bank holidays
- Option to buy or sell 5 extra annual leave days per year
- Vitality Health Insurance, including private healthcare, virtual GP access, mental‑health support and wellbeing perks
- 50% off gym memberships (Virgin Active, Nuffield, PureGym)
- Pension with 5% matched contribution
- Regular team-wide and company-wide events
- 2 volunteering days per year to give back
- Remote-first working environment with offices in London and Nottingham