AI Systems Engineer — Defence Intelligence

Elena Popescu

Contract · Mid-level · London

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About the role

POSITION TYPE: Contract, 12 months initial term with extension subject to programme continuation. CLEARANCE REQUIREMENT: Developed Vetting (DV) or SC with willingness to undergo DV processing. Candidates without existing clearance will be considered for sponsored vetting at the company\'s discretion. OVERVIEW: Our systems integration division delivers AI-enabled capability to defence, intelligence, and national security clients across the United Kingdom and Five Eyes partner nations. The programme for which this position is required involves the development of ML-assisted analysis tools for structured and unstructured data sources in time-sensitive operational environments. ROLE SUMMARY: The AI Systems Engineer will contribute to the design, development, and validation of machine learning components embedded within a larger systems architecture. The work involves both model development and integration with surrounding infrastructure in an environment where correctness, auditability, and performance under constrained compute are non-negotiable requirements. IMPORTANT: This role involves work on systems with potential operational consequence. Candidates must be comfortable operating within governance frameworks that prioritise reliability and traceability over development velocity.

Responsibilities

Develop and validate ML model components for integration within a larger defence intelligence platform
Contribute to system design documentation in accordance with programme governance requirements
Implement model evaluation and performance monitoring frameworks suitable for operational deployment
Participate in technical design reviews and contribute to risk identification and mitigation planning
Liaise with systems architects, security personnel, and client technical representatives as required

Requirements

5+ years of software or systems engineering with at least 2 years focused on applied ML in production or near-production environments
Python at a professional standard — readable, tested, documented, version-controlled
PyTorch for model development and training — experience with quantisation, distillation, or constrained inference is an advantage
Docker for containerised deployment in air-gapped or restricted-network environments
Kubernetes for orchestrating inference workloads in sovereign cloud or on-premises infrastructure
Statistics: hypothesis testing, uncertainty quantification, and the ability to characterise model failure modes clearly
Existing SC or DV clearance is a significant advantage and will expedite the hiring process

Benefits

£600 – £780 per day (inside or outside IR35 depending on engagement structure — to be confirmed at contract stage)
Initial 12-month term with strong likelihood of extension for the duration of the programme
Remote-first with periodic attendance at client sites — typically two to four days per month
Security clearance processing supported and funded by the company
Access to a well-resourced internal technical community for knowledge sharing across programmes