Machine Learning Engineer

Location: Oxfordshire/Hybrid

Position Type: Permanent + Contract Options Available

Clearance Required: SC clearance required

Our client is an expanding technology firm addressing complex, high-stakes data challenges within highly regulated sectors. They specialize in building intelligent applications that process massive volumes of Real Time data, frequently deploying software to remote, resource-constrained environments with strict power and bandwidth limitations.

We are seeking an engineer to bridge the gap between initial algorithmic models and dependable, deployment-ready software. If you want to build resilient systems that operate in critical, real-world environments, this team is the right fit.

• System Integration: Turn advanced analytical concepts into stable, scalable, operational software pipelines.

• Cross-Functional Delivery: Partner with researchers, developers, and product owners to deliver critical software features.

• Engineering Standards: Write clean, test-driven Python code, championing modern software practices within a scientific development environment.

• Technical Translation: Explain algorithmic frameworks and capabilities clearly to non-technical stakeholders and client teams.

• Technical Background: An academic degree in a quantitative field (eg, Computing, Mathematics, Physics) or equivalent commercial engineering experience.

• Python Development: Strong programming capabilities with a focus on writing maintainable, reusable, and thoroughly verified codebases.

• Infrastructure & Automation: Practical experience managing containerized environments, deployment pipelines, cloud architecture, and model monitoring tools (eg, Docker, CI/CD, Linux).

• Adaptability: Comfortable in fast-moving, evolving environments, with the flexibility to work directly with deployment teams and clients.

• Security Requirement: SC clearance required. Travel to client locations is expected.

• Background working on public sector, aerospace, or similarly regulated engineering projects.

• Familiarity with distributed computing and messaging services (such as Spark or Kafka).

• Experience developing APIs and rapid internal interfaces (such as FastAPI, Flask, Streamlit, or Gradio).

• Insight into sensor analytics, data synthesis, or model validation and reliability frameworks.