ML Ops Engineer

Depop; London; On-site; GBP 50,000 - 90,000; Full time

Job summary

An innovative company is seeking an MLOps Engineer to enhance machine learning solutions. This role involves designing and maintaining platforms for model training and deployment, ensuring operational excellence, and fostering a strong engineering culture. If you are passionate about enabling teams to leverage the power of machine learning through effective tooling and services, this position offers a fantastic opportunity to contribute to cutting-edge projects. Join a team where your skills in Python and cloud technologies will drive impactful results and continuous improvement.

Qualifications

• Proven track record in end-to-end project delivery with strong programming skills.
• Experience with ML libraries and CI/CD processes, plus cloud proficiency.

Responsibilities

• Design and maintain platforms for ML model training, deployment, and monitoring.
• Collaborate with ML Scientists and Backend Engineers to enhance tooling.

Skills

Python

Machine Learning

Communication Skills

Project Management

Cloud Platforms

Tools

TensorFlow

PyTorch

Scikit-learn

Databricks

SageMaker

Seldon

Jenkins

GitHub Actions

Docker

Kubernetes

Job description

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

• Helping design, implement and maintain 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 to maximise the value they get from the MLOps team's tooling + platform offerings.
• 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, continuous improvement and professional development.

Requirements

• Consistent track record of successful end-to-end delivery of your projects; from scoping and translating business/user requirements into plans, to design, implementation and maintenance, whilst coordinating with other teams (and engineers).
• 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.
• A strong sense of ownership, autonomy and a highly organised nature.
• Exemplary communication skills, especially in dealing with multiple stakeholders.
• Proficiency with cloud platforms (e.g., AWS, GCP, Azure) and containerization (Docker, Kubernetes)