About HASH HASH's open-source platform helps firms integrate both structured and unstructured information into knowledge graphs that support simulating, optimizing and automating processes. Our mission is to solve information failure, and help everybody make the right decisions. To that end, we're unapologetically excited. Actions speak louder than words, and we measure performance by output. We prioritize speed, and measure product delivery timelines in hours and days, not months and years. We value high-energy, high-expectations people who do what they say and say what they mean. About the role MLOps Engineers at HASH help operationalize ML models, ensuring they move seamlessly from the development phase (often in a data scientist's notebook) to a robust, continuously operating production system. We're hiring for this primarily-remote role across both the Germany (relocation support available) and the UK (existing right-to-work required). Successful candidates are also welcome to work from our offices, should they wish. Requirements Have 3+ years experience in a DevOps / Platform / MLOps / Data Engineering role. Are fluent in Python and comfortable working in a modern backend stack. Have practical experience with: Containerization and orchestration (Docker, Kubernetes or similar). Cloud platforms (AWS, GCP, Azure, or similar). CI/CD systems (GitHub Actions, GitLab CI, CircleCI, etc.). Have deployed and maintained production ML systems (e.g. model APIs, feature stores, RAG pipelines, or batch training jobs). Care about observability (Prometheus, OpenTelemetry, Grafana, etc.) and building systems you can actually debug. Think in terms of platforms and primitives, not one-off scripts – you like building tools that help other engineers move faster. Communicate clearly, enjoy collaborating across disciplines, and are comfortable in a fast-moving startup environment. Nice-to-have Familiarity with our existing infrastructure: Our current backend is largely written in Rust, and we use Temporal as an orchestration/execution engine, so a working knowledge of either is advantageous. Experience with vector databases, knowledge graphs, or RAG systems. Familiarity with LLM evaluation, prompt/agent observability, or safety/tooling around generative AI. Experience contributing to or working with open-source infrastructure or ML tooling. Background in data engineering (ETL, data quality, lineage) or security / governance in data-heavy environments. What you'll work on Design and operate ML infrastructure Integrate "traditional" ML models into agentic and other GenAI workflows. Build and maintain scalable infrastructure for training, fine-tuning, and serving models (including RAG, agents, and embeddings). Help define and standardize best practices around model deployment, rollout strategies, and versioning. Productionize AI features Partner with product and engineering teams to move prototypes into resilient, monitored production services. Implement guardrails, evaluations, and feedback loops for AI features (latency, cost, and quality). Own CI/CD for ML Extend our CI/CD pipelines to support data, model, and config changes as first-class citizens. Automate tests, checks, and validations for ML systems (data drift, schema changes, performance regression, etc.). Observability & reliability Implement metrics, logging, and tracing for model and data pipelines. Establish SLOs/SLIs for critical AI workloads and improve reliability over time. Security & governance Work with the team to make sure deployments respect HASH’s governance-first philosophy: strong permissioning, reproducibility, and traceability by default. Help standardize how models interact with knowledge graphs, private data, and third-party tools. Benefits We offer leading equity-weighted total compensation, including competitive salaries and tax-advantaged options. We also offer: Employer pension contributions At least 30 days holiday annually Twice-yearly in-person team retreats around the world HASH's founders have previously sold multiple companies (for XXm, XXXm and X billion dollar amounts).