Senior Machine Learning Operations Engineer

Deepstreamtech; United Kingdom; Remote; GBP 50,000 - 80,000; Full time

Job summary

Deepstreamtech seeks an experienced MLOps Engineer to take full ownership of the MLOps function, ensuring the development, deployment, and monitoring of machine learning models aligns with business needs. The role offers an opportunity to lead MLOps strategy and develop environments that foster growth and innovation.

Qualifications

• Experience designing scalable, production-grade ML systems.
• Hands-on expertise with MLOps tooling, such as MLflow, Kubeflow.
• Strong collaboration with data scientists and engineers.

Responsibilities

• End-to-end ownership of MLOps function.
• Design and maintain infrastructure for ML models.
• Develop tools to accelerate data scientists' work.

Skills

MLOps tooling and automation

Cross-functional collaboration

Scalable ML systems design

Technical needs identification

High-quality operations management

Tools

AWS

GCP

Docker

Kubernetes

CI/CD pipelines

MLflow

Kubeflow

DVC

Job description

Requirements

• Experience designing scalable, production-grade ML systems, including training, deployment, and monitoring using cloud platforms (e.g. AWS, GCP), Docker, Kubernetes, and CI/CD pipelines
,
• Hands-on expertise with MLOps tooling and automation, such as MLflow, Kubeflow, or DVC, to enable reproducibility, model versioning, and efficient ML workflow management
,
• Strong cross-functional collaboration skills, with a proven ability to work closely with data scientists, software engineers, and product managers to deliver aligned, production-ready ML solutions
,
• Track record of standardising ML processes, including developing internal documentation, establishing best practices, and maintaining high-quality, reliable operations
,
• Strategic mindset with a focus on scalability, capable of leading MLOps strategy and building environments that support team growth and evolving technical needs

What the job involves

• Take end-to-end ownership of the MLOps function, including identifying and prioritising technical needs
,
• Design, build, and maintain infrastructure to support the development, training, deployment, and monitoring of machine learning models
,
• Work closely with software engineers, DevOps, the data team, and product managers to ensure alignment between ML systems and business/product needs
,
• Develop tools and frameworks to accelerate the work of data scientists and machine learning engineers
,
• Ensure deployed models are integrated into production services in collaboration with software engineering teams
,
• Define and maintain documentation and internal standards for ML development, deployment, and monitoring processes
,
• Lay the groundwork for a scalable MLOps environment that supports future team growth
,
• Define and lead the strategy to bring the ML environment to a state-of-the-art standard