AI & Analytics Engineer - Healthcare AI Solutions
New
Spain, United KingdomFull-TimeMiddle
Salary not disclosed
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Job Details
- Languages
- English, Spanish
- Required Skills
- PythonSQLFastAPIREST APIData modelingDatabricksPySpark
Requirements
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related field — or equivalent practical experience
- Proven experience building AI systems in production, with real users and real-world consequences
- Strong Python engineering skills, with an emphasis on clean, testable, and maintainable code
- Deep familiarity with LLMs and GenAI patterns, including RAG, agents, structured outputs, function calling, evaluation, and monitoring
- Hands-on experience with Azure and modern cloud-native architectures (containerisation, managed services, event-driven systems)
- Comfort working across the stack — from data pipelines and APIs to stakeholder presentations
- Excellent communication skills, with the ability to tailor explanations for technical and non-technical audiences
- A proactive, ownership-driven mindset — you identify problems early and follow through to resolution
Responsibilities
- Design and deliver end-to-end AI solutions, from problem framing and architecture to deployment, monitoring, and iteration, on our Azure + Databricks medallion platform
- Architect and build LLM-powered applications, including RAG pipelines, AI agents, structured outputs, and intelligent alerting integrated with LINAC telemetry and fault data
- Develop production-grade APIs (FastAPI) exposing AI capabilities such as model inference, predictive maintenance scores, and anomaly alerts to clinical dashboards and service tools
- Translate clinical and operational requirements into robust AI system designs, acting as a bridge between domain experts (medical physicists, field engineers) and the engineering team
- Integrate ML models and GenAI components into the ViDA data platform, orchestrating inference pipelines, managing model lifecycle with MLflow, and ensuring reliability at scale
- Build and maintain data pipelines (PySpark, Delta Lake) that provide clean, validated telemetry, dosimetry, and MPC data from the global LINAC fleet
- Own production observability for AI systems, including latency, data drift, and model degradation, driving continuous improvement based on real-world performance
- Champion AI adoption across internal teams through clear documentation, technical onboarding, and explainable, actionable AI outputs
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