Jobid: 636907327869496588 (0.0502)
A multinational defence organisation is building its own applied AI capability, and
is looking for an engineer who can take a model all the way into service. You would work on real
datasets and real problems, own the pipeline around the model as much as the model itself, and do
it inside an environment where reliability and traceability matter more than novelty.
What you would be doing
- Bringing machine learning and data science methods to new datasets and new questions, and
judging honestly how well the result performs and how good the data underneath it is. - Finding what is wrong in models, pipelines and datasets, and fixing it in ways that survive
contact with production. - Designing, writing, testing, documenting and refactoring the programs, scripts and AI
components the capability is made of. - Working to the engineering standards and secure development practices of the organisation, so
that what you build can be maintained by someone else. - Supporting the whole lifecycle: gathering what is actually needed, choosing how the team
works, and automating build, test, release and monitoring. - Defining AI modules for integration, producing the build definitions and validating finished
modules against agreed functional, quality, security and performance criteria. - Building and improving the data pipelines that feed all of it, including extraction,
transformation and loading work. - Keeping colleagues informed — progress, risks and blockers — and sharing delivery ownership
through reviews rather than handovers. - Watching what is arriving in the field and contributing to technology assessments, roadmaps
and internal knowledge sharing.
What you would bring
- Hands-on history of developing, optimising, deploying and maintaining complete AI pipelines,
including training, packaging, monitoring and lifecycle management. - Strong programming alongside the machine learning: software engineering discipline applied to
applied AI work. - A solid grasp of model evaluation — how performance is measured, how it is assessed and how a
model is actually improved. - Practical use of pre-trained and foundation models, large language models and generative
techniques on problems that needed solving. - Retrieval-augmented generation, embeddings, vector stores and production agent backends, with
frameworks such as LangChain, LlamaIndex or Pydantic AI. - MLOps in earnest: version control, continuous integration and delivery, experiment and model
lifecycle practice, automated build and release. - Backend craft — REST services and modern Python with FastAPI, Pydantic or similar.
- Containers and orchestration: Docker, Kubernetes, Helm, cloud provisioning, and workflow
orchestration such as Airflow or Argo. - Guardrails and operational control for language-model systems: observability, logging and
monitoring that tell you when something has drifted. - SQL and NoSQL databases, and enough TypeScript, Node.js or Next.js to meet the front end
halfway. - Nice to have: experience of working in secure, restricted or disconnected environments.
Extensions are offered where the work goes well. Applications are reviewed as they
arrive.
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