AI Engineer

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.
• This role is for one of our project requirements

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.