AI & ML Systems Engineer

Job title - AI & ML Systems Engineer

Our client, a Cambridge based AI and ML Consultancy have an opportunity for an AI & ML Systems Engineer to join them.

The role is mainly remote working with only one day per week when the AI/ML Engineer is required to be on site in Cambridge.

Annual remuneration: Our client is willing to consider each application depending on number of years' experience

About the role:

Our client is seeking a versatile AI/ML & Systems Engineer to lead the architecture, machine learning development, and cloud integration for a next-generation asset monitoring and predictive platform. In this role,

The AI/ML Systems Engineer will bridge the gap between complex time-series telemetry, geospatial data feeds, and predictive domain models. The AI/ML Systems Engineer will be responsible for building robust data and machine learning pipelines, integrating multi-source sensor streams, and developing Real Time visualisation systems to deliver actionable structural safety insights.

This position offers the opportunity to take end-to-end ownership of scalable ML systems, from multi-modal data ingestion and distribution modelling to production-grade visualisation dashboards.

Must-have experience:

? Probabilistic Time Series Forecasting: Experience using probabilistic time series methods (eg, Bayesian, PyMC, Amazon DeepAR) on small or scarce datasets for distribution prediction.

? Data Lake & DBaaS Integration: Proven ability to build scalable cloud data ingestion pipelines and database architectures using Python, PostgreSQL (with PostGIS for spatial data), Redis, or time-series databases.

? API & Middleware Development: Experience building robust RESTful APIs and WebSocket pipelines (using FastAPI, Flask) for streaming low-latency data and alert triggers between processing backends and Front End applications.

? Experience with cloud platforms (eg, AWS, Azure, or GCP) and Docker for containerising ML applications and microservices, ensuring reproducible environments across cloud platforms.

? Version Control & CI/CD: Proficient with Git, GitHub Actions, or GitLab CI for automated testing, continuous integration, and systematic release cycles.

? Agile Methodology: Track record of working in agile, sprint-based delivery environments to hit strict technical milestones.