Data and AI Engineer- Defence

Our customer is looking for a Data and AI Engineer in the defence sector.

Role:

Undertake stocktake of current AI initiatives across Engineering UK including assessment of customer tools and infrastructure already available to determine suitability of applications for executing Systems Engineering identified use-cases.

In doing so, ensure engagement of stakeholders involved in the following activities:

External paper / conference / internet assessment of use of AI in the context of Systems Engineering and MBSE specifically to determine if any other suitable tools currently not employed by the customer are available.

Undertake Proof of Concept investigations to perform the selected use cases. This will be the main activity and may involve exploitation of tools and infrastructure already within the customer, or may involve the testing of a third party tool. Use Cases are:

Using Generative AI to produce Design Verification Plans (DVPs)

AI Generated Reference Requirements

Identification of Challenging Customer and Project Requirements

Proof of concept activities include:

Define scope of engineering data - expectation is to start with systems artefacts (Requirements, Test Results other supporting documentation as appropriate)

Scope engineering data model approach and capture mechanism in more detail

Undertake incremental definition, development and review of NLP Generative AI engineering data model as proof of concept, initially tailored to specific Systems Engineering use cases.

Ensure underlying data model compliance with customer IM and InfoSec policies.

Formalise, establish governance, deploy such that the proof of concept can be used practically to derive harmonisation and consistency into programmes in areas such as requirement definition and Verification & Validation activities.

Data Model Conclusion/Review and Decision to proceed to full deployment

Outputs:

Requirements (if external solution) or outline design solution (if internal solution) to be fulfilled by candidate AI solutions.

Down-select decision review to determine most appropriate toolsets to employ to realise Systems Engineering Use Cases follow internal/external tool assessment.

Deployment plan and pilot project deployment strategy for implementation of a Generative AI solution using natural language processing (NLP).

Incremental delivery of engineering data model - output at end of offload initial 6 month contract should cover systems engineering use cases as identified above.

Report to summarise delivery of the above - Toolset Capability Assessment, Generative AI Requirements, Down-select Justification, Summary of PoC data model design and deployment activities.

COMPETITIVE RATES OUTSIDE IR-35

Skills:

A strong understanding of the concepts, benefits, ontology, taxonomy and applications of AI.

Experience of exploring and deploying AI to support productivity improvements in the engineering domain would be an advantage.

Good experience of container management and orchestration platforms to deploy, scale and manage containerised applications is required. Direct experience of Rancher, Kubernetes or Docker would be beneficial.

Experience of data manipulation and visualisation techniques such as Power BI or Google Analytics' Looker Studio.

AI tool development skills are not necessary but some ability to code APIs to interact with Large Language Models using Python / R or other languages is required.

A strong engineering background, ideally with experience in systems engineering. Defence engineering experience is desirable.

Ability to work autonomously and comfortable with loosely defined packages of work with low levels of supervision.