Contract Lead AI Platform Engineer

Published on 11 June 2026

We are seeking to hire an experienced Contract Technical Platform Lead / Lead AI Platform Engineer – Contract. This is a 12 month assignment (inside IR35), to start 2-4 weeks.

This is a hands-on, high impact role at the intersection of AI governance, distributed systems, observability, and platform engineering to lead technical delivery for an AI centralised platform – Control Tower.

We’re looking for a Technical Lead to drive the endtoend build of this platform, shape its architecture, and partner with deep technical SMEs across the business to ensure AI systems operate with the highest levels of performance, security, and responsible oversight.

Requirements

What you’ll do:

• Lead the architecture, design, and engineering of the AI Control Tower platform.
• Build the core framework that enables AI observability, guardrails, performance monitoring, and lifecycle management.
• Shape the technical roadmap in partnership with product leaders, ensuring delivery against ambitious milestones.
• Establish engineering standards, patterns, and integration approaches used across the platform.
• Engineer a Robust, Resilient, and Secure AI Environment.Develop scalable, highthroughput data pipelines and monitoring systems.
• Ensure endtoend observability across model development, deployment, and runtime operations.
• Embed secure-by-design principles, strong IAM practices, and regulatory compliance into the platform architecture.
• Ensure all AI systems are observable, monitored, and measurable from model training through to production.
• Drive Deep Technical Collaboration and partner with SMEs across data engineering, platform engineering, security, risk, and MLOps and Independent Model Monitoring (IMM).
• Support governance of AI systems through transparent metrics, automated reporting, and integrated control mechanisms and bring thought leadership on Responsible AI, emerging standards, and new tooling.

Job responsibilities

You’ll bring a blend of strong engineering foundations, platform ownership experience, and an enthusiasm for safe and scalable AI.

Specifically:

• Strong engineering foundations, with experience building scalable distributed systems or data platforms.
• Fluency in Python, SQL, Java, and modern data processing frameworks.
• Expertise in cloud-based AI/ML ecosystems, particularly AWS SageMaker (required).
• Proven experience developing monitoring frameworks, observability pipelines, and dashboards.
• Deep understanding of event-driven architectures and messaging systems (Kafka, Vert.x, or similar).
• Knowledge of security engineering, IAM principles, encryption, and cloud security controls. Experience with CI/CD, infrastructure-as-code, and automated testing for data/ML systems.
• Exposure to MLOps, LLMOps, or model lifecycle management.

In addition:

• Awareness of model risk and regulatory frameworks (e.g., SS1/23, NIST AI Risk Management Framework).
• Understanding of operational resilience concepts and SRE practices (SLIs/SLOs).
• Experience with data lineage or governance tooling (DataHub, Glue, Collibra).
• Interest in Responsible AI, explainability, fairness/bias, and governance automation.