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Lead / Senior ML Engineer / Machine Learning Engineer / Lead Engineer / Data Pipelines / Data Science / Models / Azure / Python / Pandas / SQL / Software Engineering / DevOps / Developer / Manager / Remote / Home based / Permanent / Salary £80,000 - 110,000 + 15% bonus + benefits.
One of our leading clients is looking to recruit a Lead ML Engineer / Lead / Senior ML Engineer.
Location - Remote / Home based - may require occasional trip to London
Permanent role
Salary: £80,000 - 110,000 + 15% bonus + benefits
As well as taking a lead/senior role, you will need to be hands on developing data pipelines, taking data science prototype models to production, fix production bugs, monitor operations, and provision the necessary infrastructure in Azure.
Experience:
• Hands-on industry experience in some combination of Software Engineering, ML Engineering, Data Science, DevOps, and Cloud Infrastructure work.
• Expertise in Python which includes experience in libraries such as Pandas, scikit-learn.
• High proficiency in SQL.
• Combination of the following technologies: Python ecosystem, Azure (VMs, Web Apps, Managed Databases), GitHub Actions, Terraform, Packer, Airflow, Docker, Kubernetes, Linux/Windows VM administration, Shell scripting (primary Bash but PowerShell).
• A solid understanding of modern security and networking principles and standards.
• Bachelor’s or higher degree in Computer Science, Data Science, and/or related quantitative degree is preferred from an accredited institution.
Accountabilities will include:
• Leading Machine Learning projects end-to-end.
• Develop platform tooling (e.g., internal conda library, CLI tool for project setup, and provisioning infrastructure) for the Data Science team.
• Work with data scientists to understand their data needs and put together data pipelines to ingest data.
• Work with data scientists to take data science model prototypes to production.
• Mentor and train junior team members.
• Work with internal IT teams (security, Cloud, Global Active Directory, Architecture, Networking, etc.) to advance the team’s projects.
• Enhance code deployment lifecycle
• Improve model monitoring frameworks
• Refine project operations documentation
• Design, provision, and maintain the cloud infrastructure needed to support Data Engineering, Data Science, Machine Learning Engineers, and Machine Learning Operations.
• Write high-quality code that has high test coverage.
• Participate in code reviews to help improve code quality.
Technologies/Tools: Python, Azure (Virtual Machines, Azure Web Apps, Cloud Storage, Azure ML), Anaconda packages, Git, GitHub, GitHub Actions, Terraform, SQL, Artifactory, Airflow, Docker, Kubernetes, Linux/Windows VMs.
