Senior AI​/ML Engineer

What we’re looking for Great AI and machine learning capability only creates value once it runs reliably in the real world. This role exists to bridge that final, critical gap—taking the AI/ML solutions developed within our Data Science team from promising prototypes to robust, scalable production systems that our teams and customers depend on every day.

Sitting within Data Science, this is at heart a hands-on AI/ML engineering role. Your primary focus will be product ionising Generative AI and agentic systems—building the APIs, tools, integrations and workflows that let LLMs, and agents operate reliably  will also support the broader range of machine learning models and pipelines across the business. You will be the key partner working hand in hand with Engineering, Dev Ops and Infrastructure teams to bring our models and systems into production—owning the technical journey of making AI/ML systems live, stable, secure and performant.

As a senior member of the Data Science team, you will set the standard for production readiness, act as the bridge between data science and the wider engineering organisation, drive delivery across teams, and mentor colleagues. Your work will directly determine how quickly and confidently Argus can bring new AI/ML capabilities to life.

What will you be doing

Delivery & Engineering Design and build robust, secure APIs and backend components that power AI/ML, GenAI and agentic applications.

Engineer agentic systems—integrating LLMs with tools, data sources, and business workflows into reliable, production-grade pipelines.

Drive systems from prototype to production, owning reliability, scalability, and operational readiness.

Raise code quality, structure, and production readiness across the AI/ML stack.

Debug and resolve issues across APIs, environments, and integrations, ensuring rapid response times and minimal disruption.

Collaboration & Partnership Act as the primary technical partner between Data Science and the Engineering, Dev Ops and Infrastructure teams, taking solutions from development through to live deployment.

Advise and support colleagues across the business whose systems integrate with AI/ML and agentic components.

Proactively resolve technical ambiguity to reduce delivery friction and rework, ensuring a smooth handover from prototype to production.

Technical Leadership & Enablement Establish and evolve engineering standards for product ionising AI/ML, including testing, observability, versioning, and release management.

Mentor and guide data scientists and engineers, providing code reviews and hands-on technical support.

Champion a culture of disciplined engineering, continuous improvement, and operational excellence within Data Science.

Skills and Experience

EducationA degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Data Science, or a related technical discipline—or equivalent hands-on experience. An MSc or PhD is welcome but not essential.

Essential Experience & Skills Exceptionally strong Python programming skills, with a deep grasp of object-oriented design, clean code, and core software engineering principles (e.g. SOLID, design patterns, modularity, testability).Strong backend / API engineering experience, ideally in Python (e.g. FastAPI, or similar).Hands-on experience building and operating solutions in AWS environments.

Proficiency with Docker, Git Hub, and CI/CD pipelines.

Proven ability to partner with and work across teams to drive delivery into production.

Strong problem-solving, debugging, and performance optimisation skills.

Solid software engineering foundations, including version control, automated testing, and monitoring.

Desirable Experience building or product ionising agentic AI systems—tool use, orchestration, multi-step reasoning, or agent frameworks (e.g. Lang Graph, Lang Chain, CrewAI, Auto Gen, or similar).

Experience with GenAI / LLM systems in a production context (RAG, prompt orchestration, evaluation, guardrails, cost/latency optimisation).Exposure to MCP (Model Context Protocol) and tool-based / function-calling architectures.

Experience deploying and operating machine learning models / MLOps…