Senior MLOps Engineer

Your key responsibilities 
Reporting to the Director of Engineering you will be responsible for building the foundation of our MLOps capabilities, working closely with Data Scientists, Data Engineers and multiple Technology Service departments to manage the end-to-end ML lifecycle including automation of machine learning workflows, code deployments, testing, and data validation processes.
The candidate should be well-versed in using tools like Jenkins, GitLab CI, Azure DevOps, or equivalent, and should be able to integrate these tools with machine learning platforms and data repositories.

Functional
•    Design, implement, and manage CI/CD pipelines tailored for machine learning workflows.
•    Ensure that machine learning models are properly versioned and deployed into production, staging, or testing environments automatically.
•    Collaborate with data scientists and software engineers to optimize the automation process for training, validating, and deploying machine learning models.
•    Continuously monitor the performance and reliability of CI/CD pipelines and make adjustments as necessary.
•    Set up environments and fully implement scalable machine learning operations.
•    Continuously monitor, optimize, debug and automate MLOps pipelines for increased quality and efficiency—including pipeline level, module level, and system-level inspection.
•    Develop & maintain the infrastructure & tools that facilitate the deployment and monitoring of our ML algorithms in production.
•    Keep abreast of the latest technology trends to drive standard methodologies and stay ahead of the curve.
•    Document and track all systems, pipelines and best practices.

Skills and attributes for success 
The measurement of your success will be based on cost savings, the impact of MLOps on business revenue, the number of projects successfully completed, improvements made to existing solutions and processes, and customer Net Promoter Score (NPS).

To qualify for the role you must have 
•    10+ years of Machine Learning Experience.
•    Minimum of 5 years of experience in setting up and maintaining CI/CD pipelines, preferably in a machine learning context.
•    Strong understanding of containerization and orchestration tools such as Docker and Kubernetes.
•    Familiarity with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
•    Proficient in scripting languages such as Python, Bash, or Shell for automation tasks.

Ideally, you’ll also have 
•    ML Training data lifecycle.
•    Data storage and management engines: NoSQL databases; Search engines e.g. Elasticsearch, SQL databases; Timeseries databases.
•    Knowledge of distributed computer systems and web software development
•    Outstanding communication and presentation skills