Vessel & Vector Ltd
AI ML Engineer
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Location: Cambridge / Hybrid (UK only)
Compensation: Highly competitive
Sponsorship: No sponsorship provided. The candidate should be able to prove and maintain their own legal work status throughout the appointment.
Employment Type: Full-time, fixed-term contract. Initial contract will be for 3 months.
About the role
We are seeking a versatile AI/ML & Systems Engineer to lead the architecture, machine learning development, and cloud integration for a prototype asset monitoring and risk prediction platform. In this role, you will bridge the gap between complex time-series telemetry, geospatial data feeds, and predictive domain models. You will be responsible for building 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 a high-value prototype ML system, from multi-modal data and distribution modeling to interactive real-time visualisation dashboards.
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Must-haves
- Probabilistic Time Series Forecasting: Experience using probabilistic time series methods (e.g., Bayesian, PyMC, Amazon DeepAR) on small or scarce datasets for distribution prediction.
- DBaaS Integration: Proven ability to build scalable data pipelines and database architectures using Python, PostgreSQL (with PostGIS for spatial data), Redis, or time-series databases.
- At least one of the following:
- Vibration Analysis: Experience or knowledge of vibration analysis, digital signal or waveform processing (DSP).
- Physics-Informed Models or Neural Networks: Experience with combining data-driven sequence modelling with physical laws.
- Agile Methodology: Track record of working in agile, sprint-based delivery environments to hit strict technical milestones.
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Good-to-haves
- Familiarity with frontend visualisation frameworks (e.g., React, Grafana, or Plotly/Dash) for building real-time data, geospatial heatmaps, and analytical dashboards.
- Experience with cloud platforms (e.g., AWS, Azure, or GCP) or Docker for containerising ML applications and microservices, ensuring reproducible environments across cloud platforms.
- 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 frontend applications.
- Version Control & CI/CD: Proficient with Git, GitHub Actions, or GitLab CI for automated testing, continuous integration, and systematic release cycles.
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