Job Description

At Depop, Machine Learning is integral to our growth.

We are looking for an MLOps engineer to help level-up how ML solutions are delivered at Depop.

As an MLOps engineer you will be responsible for enabling our data scientists - currently spread across four product functions - to deliver value by providing platforms and services for ML model + feature development, deployment and monitoring. Do you find joy 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

You’ll be...

• Helping design and implement systems that can ensure continued improvements to deployments speed of data scientists
• Help to define, create and support the technical patterns data scientists use to deploy to production - including but not limited to: training/prototyping, feature management, model deployment, & model serving/endpoints - for real-time and batch implementations.
• Hold high 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, and professional development.

Qualifications

Requirements

You have…

• Proven track record of successful end-to-end delivery of your projects; from scoping and translating business requirements into plans, to design, implementation and maintenance, whilst coordinating with other teams (and engineers).
• Experience with the core concepts of data science / ML workflows
• Experience working in a B2C product-based technical environment
• A strong sense of ownership, autonomy and a highly organised nature.
• Exemplary communication skills, especially in dealing with multiple stakeholders
• Strong knowledge of systems design within a modern cloud-based environment (AWS, GCP)

Technologies and Tools

• Data science / MLOps tooling: e.g. Sagemaker, Databricks, TFServing and more
• AWS - IAM, S3, redis, ECS and more
• Python
• Terraform
• Shell scripting and related tooling
• Kubernetes
• Good working understanding of continuous integration/deployment tools and practices
• Experience with streaming and/or batch-based systems supporting data integrations to third-party platforms (e.g. using Kafka, Airflow, RMQ, etc.)