AI Engineer – AI Maintenance & Technology Upgrades
We are seeking an experienced AI Engineer to lead the maintenance, modernisation, and continuous improvement of our production AI services. The role will own the technical delivery of AI model upgrades and enabling technology upgrades, ensuring that changes are secure, reliable, scalable, well-governed, and implemented with minimal disruption to business operations.
The successful candidate will provide hands-on technical leadership across the AI service lifecycle—from upgrade assessment and solution design through testing, release, production validation, and ongoing optimisation. The candidate will work closely with Business, Data, Technology, Architecture and Risk teams to maintain resilient and compliant AI capabilities.
Key Responsibilities:
AI Model Upgrade Leadership
- Own the end-to-end roadmap and execution of upgrades across machine learning, Generative AI, large language model, and related AI services.
- Assess new model versions, and capabilities against business needs, performance, security, compatibility, and governance requirements.
- Define evaluation criteria and oversee benchmark, regression, safety, performance, and business acceptance testing before production release.
- Lead migration and rollout strategies, including release sequencing, rollback planning, change controls, and post-implementation validation.
- Ensure prompts, configurations, datasets, evaluation assets, and model versions are controlled, traceable, and appropriately documented.
Technology Modernisation & Platform Upgrades
- Lead upgrades to application frameworks, libraries, APIs, runtime environments, cloud services, data components, and AI/ML platforms supporting departmental AI services.
- Evaluate technical debt, dependencies, and end-of-life risks; translate findings into prioritised remediation plans.
- Partner with enterprise architecture and infrastructure teams to ensure solutions align with technology strategy and production standards.
Governance, Risk & Controls
- Embed responsible AI, model risk, cybersecurity, data privacy, change management, and audit requirements throughout the upgrade lifecycle.
- Maintain complete technical artefacts and evidence, including architecture decisions, model cards, test results, approvals, release records, and operational procedures.
- Identify and manage technical, model, operational, and third-party risks; escalate material issues and drive timely remediation.
- Lead investigation and resolution of complex production incidents, including root cause analysis, corrective actions, and preventive improvements.
Requirements
Education & Experience
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Information Technology, or a related discipline.
- 12–16 years of relevant experience in software engineering, AI/ML engineering, data engineering, platform engineering, or related technology roles.
- Significant experience leading enterprise-scale AI or ML platforms and production services, including complex model and technology upgrades.
- Demonstrated experience delivering and operating AI applications in large, regulated, or operationally critical environments.
- Strong track record of leading multidisciplinary technical teams and managing senior stakeholders, external vendors, and technology partners.
Technical Skills
- Deep understanding of the AI/ML lifecycle, including model evaluation, deployment, versioning, monitoring, retraining, rollback, and retirement.
- Strong knowledge of Generative AI, large language models, retrieval-augmented generation, prompt engineering, agentic workflows, and responsible AI considerations.
- Proficiency in Python and experience with common AI/ML frameworks, model APIs, orchestration tools, and data processing libraries.
- Hands-on experience with MLOps or LLMOps platforms, CI/CD pipelines, model registries, automated evaluation, containerisation, and infrastructure-as-code.
- Strong knowledge of APIs, microservices, distributed systems, databases, vector stores, identity and access controls, security, logging, and observability.
- Ability to diagnose model and system performance issues across application, data, infrastructure, and integration layers.
Preferred Qualifications
- Experience in banking, financial services, operations, or another highly regulated industry.
- Experience implementing model governance, AI risk controls, security reviews, audit evidence, and formal change management processes.
- Relevant certifications in cloud architecture, AI/ML engineering, cybersecurity, DevOps, or technology delivery.
- Experience managing multi-model or multi-provider AI environments and evaluating emerging AI technologies for enterprise adoption.