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.