Mission

Own the glue layer that turns MFL’s AI components into a single, production‑ready platform used daily by scientists, chefs and business teams.

Key Responsibilities

· Develop and maintain full‑stack applications (Python/FastAPI backend, React or Angular/React frontend) that expose graph insights and AI services via secure APIs and intuitive UIs.

· Integrate ML & LLM pipelines with the knowledge graph - deploy trained models, orchestrate inference, and monitor performance in real time.

· Translate domain requirements from flavour scientists and data experts into functional, scalable technical solutions.

· Ensure performance, scalability and security, especially when handling proprietary R&D data.

· Implement DevOps best practices: container services with Docker, deploy to Kubernetes, automate CI/CD with GitHub Actions or ETLs

· Act as the company’s “technical glue” connecting data ingestion, graph storage, ML serving, and frontend layers, and mentoring peers on integration patterns.

Requirements

· Proven experience as a Full‑Stack Engineer (5 + years), shipping production software in a cloud environment.

· Strong proficiency in Python, with production experience using FastAPI. C++ is a bonus.

· Graph‑tech mastery: hands‑on with Neo4j (Cypher, APOC, GDS) and experience optimising queries, indexes and data models.

· Frontend skills: React/Next.js or Angular; capable of crafting data‑visualisation and interactive dashboards.

· ML integration: familiarity with frameworks such as PyTorch, TensorFlow, and deploying models to production.

· Cloud & DevOps: Docker, Kubernetes, observability with Grafana.

· Soft skills: excellent communication, bridging technical and non‑technical stakeholders, strong problem‑solving, and ownership.