Ai Ml Engineer

## About the Role We are looking for a AI / ML Engineer to design and build the downstream intelligence layer on top of QUIPU's living knowledge graph. This is not a standard ML engineering role. You will work directly on a system where every entity has a full event-sourced history, every relationship is typed and versioned, and every query can traverse years of connected data in milliseconds. You will build the algorithms and pipelines that turn this living graph into predictions, prognostics, simulations, and autonomous reasoning — the layer that makes QUIPU not just a data platform, but an intelligence engine. ## Responsibilities - Design and build the downstream intelligence layer on top of QUIPU's living knowledge graph. - Build the algorithms and pipelines that turn this living graph into predictions, prognostics, simulations, and autonomous reasoning. ## Requirements ### Must-have Technical Competencies #### Core ML / AI - 5+ years in applied ML / AI engineering - Bayesian methods, MCMC, probabilistic modelling - Reinforcement learning (offline RL, policy gradient) - Time-series modelling, survival analysis - Graph neural networks (GNN, GAT, GraphSAGE) - Causal inference methods #### Engineering & Systems - Python, Scala or JVM-based ML pipelines - Graph databases or graph traversal algorithms - Event-driven or streaming data architectures - Distributed compute (Spark, Ray, or similar) - MLflow, experiment tracking, model versioning - Experience with knowledge graphs a strong plus ## Nice to Have - Experience with graph-based anomaly detection and fraud analytics. - Knowledge of NLP and Knowledge Graphs. - Exposure to MLOps and CI/CD for model deployment. ## Domain Exposure You do not need to be a domain expert. But prior exposure to any of the following will accelerate your impact: - Biomedical engineering, computational biology, or cheminformatics pipelines - Predictive maintenance or IoT analytics in manufacturing or automotive - Clinical trial data, electronic lab notebooks, or LIMS systems - Regulatory environments requiring explainable, auditable AI (FDA, EU AI Act) ## What We Offer - Work at the intersection of graph intelligence, event sourcing, and frontier AI applications - Direct influence on architecture — this team is small and every engineer shapes the product - A genuinely novel technical problem space — no established playbook, real research opportunities - Anchor customers in pharma and automotive — your models will affect real drug development decisions - Competitive compensation with meaningful equity at an early stage