Job Description

Machine Learning Ops Engineer

Contract role

Inside IR35

2 days a week on site in London 

Job Overview:

We are seeking an experienced ML Ops Engineer to lead the deployment, management, and optimization of machine learning models. The ML Ops Engineer will be accountable for ensuring models are production-ready, streamlining processes, and automating deployments to guarantee the efficient operation of the models. This role involves close collaboration with data science, development, and support teams to ensure smooth integration, adherence to best practices, and compliance with security and data governance standards.

Key Skills and Qualifications:

- Proven ability to balance multiple competing tasks and prioritize effectively within a development team.

- Strong communication skills to explain technical concepts to non-technical stakeholders and to facilitate collaboration across teams.

- Experience with negotiation and conflict resolution, determining scope, and prioritization for projects.

- Expertise in machine learning concepts, frameworks, and ensuring seamless integration and scalability.

- Ability to ensure data scientists can easily use ML models without worrying about their maintenance or deployment.

- At least 3 years of experience as an ML Ops Engineer on projects of similar scope and scale

- Strong technical experience in:

- Deploying ML models using Azure stack.

- Programming in Python and Scala in the context of ML models.

Key Responsibilities:

Review and Optimize Proof of Concept:

- Collaborate with the Data & Analytics (D&A) Product Owner to understand project requirements.

- Review the Data Science proof of concept, ensuring it is ready for production.

- Provide feedback on necessary changes for optimal production deployment.

Model Deployment and Pipeline Creation:

- Develop and create pipelines for model deployment in collaboration with the D&A Development team.

- Manage, deploy, and optimize the model in a production environment to ensure efficient operations.

CI/CD Pipeline Monitoring

- Manage deployment pipelines for ML models and trigger CI/CD pipelines.

- Monitor pipelines to ensure all tests pass and model outputs are delivered to the correct locations.

- Review code changes and pull requests from the D&A Data Science team, moving them forward in a controlled manner.

Implement Best Practices

- Enforce security and data governance best practices to safeguard models and data.

- Establish Business-as-Usual (BAU) processes, build a ways-of-working guide, and implement monitoring tools to maintain response times within set tolerances.

Cross-Team Collaboration:

- Act as a conduit between the Data Science team and TSO support team, ensuring production issues are resolved within the agreed Service Level Agreements (SLAs).

- Engage with the Senior Product Owner, Data Engineers, and Technical Delivery Manager to identify dependencies and integration points across different areas.

Continuous Improvement:

- Work independently and as part of a team to develop scalable, efficient, and easy-to-maintain solutions.

- Continuously assess and improve product processes, enhancing efficiency, product development, agile practices, and product strategy.

Technology Expertise:

- Ensure proficiency in Azure technologies, with particular expertise in Python and Scala, for successful model deployment.

- Work closely with the Data Science team on model review, code refactoring, containerization, and versioning to maintain high-quality standards.

Stakeholder Communication:

- Effectively communicate complex technical concepts to non-technical stakeholders, ensuring clarity across teams including Developers, Build Managers, TSO Support teams, Delivery Managers, and BI/Data Architects.

- Interface comfortably with various bespoke data sources, other program areas, and third parties when necessary