Salary: £43,000 - 83,000 per year
Requirements
- 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.
- Exceptionally strong Python programming skills, including object-oriented design, clean code, and software engineering principles such as SOLID, design patterns, modularity, and testability.
- Strong backend and API engineering experience, ideally with Python frameworks such as FastAPI or similar.
- Hands-on experience building and operating solutions in AWS environments.
- Proficiency with Docker, GitHub, and CI/CD pipelines.
- Proven ability to work across teams and 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 productionising agentic AI systems, including tool use, orchestration, multi-step reasoning, or agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or similar.
- Desirable: Experience with production GenAI or LLM systems, including RAG, prompt orchestration, evaluation, guardrails, and cost or latency optimisation.
- Desirable: Exposure to MCP (Model Context Protocol) and tool-based or function-calling architectures.
- Desirable: Experience deploying and operating machine learning models or MLOps pipelines, such as model serving, monitoring, and retraining workflows.
- Desirable: Experience with real-time or streaming systems.
Responsibilities
- Design and build robust, secure APIs and backend components that power AI/ML, GenAI, and agentic applications.
- Engineer agentic systems by integrating LLMs with tools, data sources, and business workflows into reliable, production-grade pipelines.
- Take systems from prototype to production, owning reliability, scalability, and operational readiness.
- Improve code quality, structure, and production readiness across the AI/ML stack.
- Debug and resolve issues across APIs, environments, and integrations to support rapid response and minimise disruption.
- Partner with Data Science, Engineering, DevOps, and Infrastructure teams to take solutions from development through live deployment.
- Advise and support colleagues across the business whose systems integrate with AI/ML and agentic components.
- Resolve technical ambiguity proactively to reduce delivery friction and rework and ensure smooth handovers from prototype to production.
- Establish and evolve engineering standards for productionising AI/ML, including testing, observability, versioning, and release management.
- Mentor and guide data scientists and engineers through code reviews and hands‑on technical support.
- Promote disciplined engineering, continuous improvement, and operational excellence within our Data Science team.
Technologies
- Agentic AI
- AI
- API
- AWS
- Backend
- CI/CD
- DevOps
- Docker
- FastAPI
- GitHub
- Support
- LLM
- Machine Learning
- MCP
- MLOps
- Model Serving
- Python
- RAG
- Cloud
More
We are Argus, a rapidly growing, award‑winning business. This hands‑on AI/ML engineering role sits within our Data Science team and focuses on taking AI/ML solutions, especially generative AI and agentic systems, from prototypes to reliable production systems. You will help set production‑readiness standards and bridge Data Science with our wider engineering organisation. We offer a dynamic environment for talented, entrepreneurial professionals to grow their careers, recognise and reward successful performance, and promote professional development as an Investor in People. Benefits include a competitive salary and company bonus scheme, group pension, group healthcare and life assurance, hybrid working (currently one day in the office), 25 days of annual holiday increasing incrementally up to 30 days, subsidised gym membership, a season‑ticket travel loan, a cycle‑to‑work scheme, a flexible benefits platform, and extensive internal and external training.
last updated 40 week of 2026
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