Senior MLOps Engineer

The Role

At Depop, machine learning is integral to our value proposition. We are looking for a Senior MLOps engineer to help level-up how ML solutions are delivered at Depop.

As a Senior MLOps engineer sitting in the central MLOps team, you will be responsible for enabling our ML Scientists - currently spread across five product functions - to deliver value by providing self-serve platforms and services for ML model + feature development, deployment and monitoring. 

Do you find happiness in providing tooling, services and platforms that help businesses untap the enormous value of machine learning? If so, this could be the perfect match.

Want to find out more about Depop & our engineering team? We write about technology, people and smart engineering right here -https://engineering.depop.com/

Responsibilities

• Leading on the design, implementation and maintenance of tooling + platforms for:

  • Productionising model training workflows

  • ML feature engineering and deployment

  • Deploying, monitoring and managing ML models in production

  • Model performance monitoring and drift detection

  • Model retraining, rollback, and continuous improvement

• Playing an enablement role by working closely with ML Scientists and Backend Engineers whilst also finding opportunities to improve the velocity + reliability of the ML model deployment cycle.

• Proactively identify pain points and make improvements that help increase your team’s efficiency.

• Using your extensive domain knowledge + relationships with key stakeholders to positively influence the direction of the team.

• Setting the team’s standards for operational excellence; from running your own services to testing, monitoring, maintenance and reacting to production issues.

• Adding to a strong engineering culture orientated on technical innovation, continuous improvement and professional development.

• Mentoring junior engineers to help them hit their career goals and add further value to Depop.

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Requirements

• Consistent track record of leading on the successful end-to-end delivery of your projects; scoping and translating complex business/user requirements into plans, with a focus on MvP; design and implementation (including coordinating the effort of other engineers); and maintenance, with a strong emphasis on observability and handling failure modes.

• Solid understanding of the ML lifecycle, from model training and evaluation to deployment and monitoring.

• Strong programming skills in Python, with experience in ML libraries such as TensorFlow, PyTorch and Scikit-learn.

• Experience working with ML training/inference platforms such as Databricks, SageMaker and Seldon.

• Experience building CI/CD processes with tools such as Jenkins or GitHub Actions.

• Exemplary communication skills, especially in dealing with multiple stakeholders.

• Experience with cloud platforms (e.g., AWS, GCP, Azure), containerization (Docker, Kubernetes) and infrastructure-as-code (IaC) tools (Terraform, CloudFormation).