Principal MLOps Engineer leading design and optimization of machine learning infrastructure at Wood Mackenzie. Collaborating with data science and engineering teams to ensure robust automated ML lifecycles.
Responsibilities
- be continuously developing a class leading data platform infrastructure
- be part of a larger centre of excellence of data specialists, looking after our Data, Business Intelligence, and ML platforms
- work in an agile environment, contributing ideas and helping to prioritise technical work
- be working in a very friendly small team that values your ideas, where you have the chance to grow, explore and contribute
- work on a mixture of platform work and greenfield project initiatives
- mentor Junior and Mid Level Engineers
- work as part of an international team, driving our class leading data, ML and AI platform forwards as well as developing greenfield data products
Requirements
- strong programming skills, preferably in Kotlin, Python, Spring, Snowflake, Kafka, Kafka streams, Databricks, Flink, Airflow, Airbyte and AWS
- experienced with Kafka, Kafka Streams, relational databases, Docker, and AWS products – or has an enthusiasm to learn
- a good communicator, with the ability to convey complex technical concepts
- experienced with working with Data Scientists, an understanding of ML models and experience of putting models in production
- proactive in suggesting new tools, architecture, processes, and technologies
- great to work with, proactive, and has good interpersonal skills
Benefits
- 25 days annual leave (plus bank holidays)
- the option to buy five more days of holiday with your flexible benefits
- flexible parental leave with six months full pay to the primary caregiver and four weeks full pay if you’re the secondary caregiver
- life event leave with an extra day's leave every two years
- a two week paid sabbatical after five years of service
- four weeks off after ten years of service
- private medical insurance through BUPA
- health cash plan that covers everyday medical expenses
- access to tools for personal development
- support for mental health with access to counselling
- matched pension contribution up to five per cent
- tax-free life assurance policy
- salary sacrifice car scheme
- flexible benefits scheme
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
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