About Us
We are a VC-backed startup focused on hyper-personalisation, currently in stealth. Inspired by the latest in recommender systems, we leverage transformers and graph learning alongside decision-making models to build the most engaging customer experiences for in-store retail.
Our mission is to change retail forever through hyper-personalised experiences that are both simple and beautiful.
About the Role
As a Senior MLOps Engineer, you will own the infrastructure and tooling that turns experimental models into dependable production systems. You will build the pipelines, monitoring, and deployment workflows that allow our research engineers to move fast without breaking things. If you want to operate at the intersection of machine learning and production systems engineering, this role is for you.
Key Responsibilities
* Build and maintain CI/CD pipelines for model training, evaluation, and deployment across research, staging, and production environments.
* Design and implement model registries, versioning systems, and experiment tracking to ensure full reproducibility of all model releases.
* Deploy ML workflows using tools like Airflow or similar, managing dependencies from data ingestion through model deployment and serving.
* Instrument comprehensive monitoring for model performance, data drift, prediction quality, and system health.
* Manage infrastructure as code (Terraform, or similar) for compute resources, ensuring efficient scaling across training and inference workloads.
* Collaborate with research and engineering teams to establish system SLOs/SLAs aligned with business objectives.
* Build tooling and abstractions that make it easy for Research Engineers to deploy models reliably without needing deep infrastructure knowledge.
* Ensure compliance, governance of all ML processes and workflows.
Essential Qualifications
* Experience building and operating ML infrastructure, ideally in production environments serving real users.
* Strong proficiency in containerisation (Docker, Kubernetes) and orchestration of multi-stage ML workflows.
* Hands-on experience with ML platforms and tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or similar model management systems.
* Practical knowledge of infrastructure as code, CI/CD best practices, and cloud platforms (AWS, GCP, or Azure).
* Experience with relational databases and data processing and query engines (Spark, Trino, or similar).
* Familiarity with monitoring, observability, and alerting systems for production ML (Prometheus, Grafana, Datadog, or equivalent).
* Understanding of ML concepts. You don't need to train models, but you should speak the language of Research Engineers and understand their constraints.
* A mindset that balances reliability with velocity: you care about reliability and reproducibility, but you also enable teams to ship fast.
Desired Skills (Bonus Points)
* Experience delivering API services (FastAPI, SpringBoot or similar).
* Experience with message brokers and real-time data and event processing (Kafka, Pulsar, or similar).
What We Offer
* Opportunity to build technology that will transform millions of shopping experiences.
* Real ownership and impact in shaping product and company direction.
* A dynamic, collaborative work environment with cutting-edge ML challenges.
* Competitive compensation and equity in a rapidly growing company.