About the Role:
As a Machine Learning Operations (MLOps) Engineer, you’ll beresponsible for managing, releasing and monitoring Machine Learning (ML) andArtificial Intelligence (AI) artefacts using automated frameworks. You’ll alsooptimise ML/AI code written by our Data Scientists into Production-readysoftware according to agreed performance and cost criteria.
You’ll play a key role ensuring that ML/AI projects are setup forsuccess via the automation of residual manual steps in the development andproduction lifecycle. You’ll also provide essential insights into the ongoingpredictive capability and cost of deployed ML/AI assets using language andvisualisations appropriate for your audience.
It’s an opportunity to work across multiple projects concurrently. You’lluse your judgement to determine which projects and teams need most of yourtime. You’ll contribute to early engagements through strong communicationskills, domain experience and knowledge gathered throughout your career.
This role requires you to have adept time-management and prioritisationskills to keep on top of your responsibilities. You’ll use your cross-projectexposure to feedback to the Data & Data Science Leadership Team to guideunderstanding, improve consistency, and develop & implement initiatives toimprove the community for the future.
Who we’relooking for
You’llneed strong experience delivering and monitoring and scalable ML/AI solutionsvia automated ML Ops.
Ideally,you’ll also be technically skilled in most or all of the below:
- Expert knowledge of Python and SQL, inc. the following libraries: Numpy, Pandas, PySpark and Spark SQL
- Expert knowledge of ML Ops frameworks in the following categories:
a) experiment tracking and model metadata management ( MLflow)
b) orchestration of ML workflows ( Metaflow)
c) data and pipeline versioning ( Data Version Control)
d) model deployment, serving and monitoring ( Kubeflow)
- Expert knowledge of automated artefact deployment using YAML based CI/CD pipelines and Terraform
- Working knowledge of one or more ML engineering frameworks ( TensorFlow, PyTorch, Keras, Scikit-Learn)
- Working knowledge of object-oriented programming and unit testing in Python
- Working knowledge of application and information security principles and practices ( OWASP for Machine Learning)
- Working knowledge of Unix-based CLI commands, source control and scripting
- Working knowledge of containerisation ( Docker) and container orchestration ( Kubernetes)
- Working knowledge of a cloud data platform ( Databricks) and a data lakehouse architecture ( Delta Lake)
- Working knowledge of the AWS cloud technology stack ( S3, Glue, DynamoDB, IAM, Lambdas, ELB, EKS)
Rewards andbenefits
As you help us to shape the future, we’ve shaped our rewards and benefits tohelp you thrive and support your lifestyle:
- Competitive salary
- Discretionarygroup performance-based bonus
- 25 days annual leave (plus Bank Holidays)
- Single cover private medical insurance
- Pension scheme
We’re committed to making a tangible impact on the climate challenge we allface. Drax is where your individual purpose can work alongside your careerdrive. We work as part of a team that shares a passion for doing what’s rightfor the future. With Drax you can shape your career and a future forgenerations to come.
Together, we make it happen.
At Drax, we’re committed to fostering an environment where everyone feelsvalued and respected, regardless of their role. To make this a reality, weactively work to better represent the communities we operate in, fosterinclusion, and establish fair processes. Through these actions, we build thetrust needed for all colleagues at Drax to contribute their perspectives andtalents, no matter their background. Find out more about our approach here.
How to apply
Think this role’s for you? Click the ‘Apply now’ button to begin your Draxjourney.
If you want to find out more about Drax, check out our LinkedIn page to see ourlatest news.
