Senior AI/ML Engineer
Who we are
About this role
What you’ll do
- Design and implement production machine-learning, generative-AI and justified agent-based services, including inference, feature processing, serving and integration.
- Productionise validated ML, generative-AI and agent-based solutions using approved Data Platform capabilities.
- Establish MLOps and LLMOps practices covering versioning, testing, automated evaluation, continuous delivery, deployment, rollback and environment promotion.
- Implement proportionate AI evaluation covering quality, safety, hallucination, bias, robustness and task performance.
- Own production monitoring and operational readiness across drift, latency, availability and cost.
- Establish appropriate guardrails, runbooks, incident-response processes and audit evidence for production AI services.
- Partner with the Lead Data Scientist and Data Engineers to translate validated scientific approaches into reliable production services.
- Evaluate build-versus-buy decisions and provider trade-offs, considering performance, security, portability, reliability, provider dependency and cost.
- Apply appropriate privacy, security, human oversight, explainability, safety and model-risk controls throughout the production lifecycle.
- Use AI-assisted engineering practices while maintaining human accountability, validation, traceability and production discipline.
Who you are
- Substantial practical experience in software, platform or ML Engineering, including production ML/AI delivery.
- Strong Python and software-engineering capability across APIs, automated testing, containers and continuous delivery.
- Experience with Vertex AI or equivalent managed ML platforms and model-serving services.
- Practical experience implementing MLOps, including model registries, versioning, deployment, rollback and monitoring.
- Relevant experience with generative-AI evaluation and LLMOps.
- Knowledge of responsible AI engineering, including privacy, security, human oversight, explainability, safety and model-risk controls.
- Strong understanding of APIs, containers, cloud services and production observability.
- An understanding of statistical and machine-learning concepts sufficient to identify and challenge invalid production assumptions.
- Sound production judgement when balancing reliability, latency, scalability, portability, provider dependency and cost.
- The ability to collaborate effectively with Data Scientists and Data Engineers to convert validated approaches into controlled production services.
Not a perfect fit?
What's in it for you
- The opportunity to engineer and productionise machine-learning, generative-AI and justified agent-based solutions across scalable digital services.
- Ownership across the ML/AI production lifecycle, including deployment, evaluation, observability, operational readiness and control.
- The opportunity to establish and mature MLOps and LLMOps delivery practices.
- Practical experience applying responsible AI engineering across quality, safety, hallucination, bias, robustness, privacy and security.
- The opportunity to influence technical decisions across model serving, tooling, provider selection and build-versus-buy choices.
- Collaboration with Data Science and Data Engineering specialists on the transition from validated approaches to controlled production services.
- Experience balancing real-world AI engineering considerations including reliability, latency, scalability, portability, provider dependency and cost.
- Flexible UK working across Newbury and Paddington.
What skills you will learn
- Production engineering for machine-learning and generative-AI systems.
- MLOps and LLMOps, including automated evaluation, model versioning, deployment, rollback and environment promotion.
- Production AI evaluation covering hallucination, bias, safety, robustness, quality and task performance.
- Cloud-based ML and AI engineering using Google Cloud Platform, Vertex AI or equivalent technologies.
- Model and AI-service observability across drift, latency, availability and cost.
- Engineering and operational controls for agent-based AI solutions.
- Responsible AI engineering covering security, privacy, explainability, human oversight and model-risk controls.
- Production incident response, runbook development, guardrails and audit evidence.
- Technical assessment of build-versus-buy, portability and AI-provider trade-offs.
VOIS Equal Opportunity Employer Commitment
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