Senior Machine Learning Ops Engineers (PROJ-4472)
Job Description
Remote is seeking Senior Machine Learning Ops Engineers to join the team in ASD. The Senior Machine Learning Ops Engineers will be involved in setting the overall ML Ops strategy for the organisation as well as the delivery of complex projects. They will work closely with cross-functional teams including data scientists, engineers and business stakeholders to ensure that ML initiatives are aligned with industry standards and business goals.
The Senior Machine Learning Ops Engineers will be responsible for building and maintaining ASD’s ML Operations platform. This position is well suited to candidates with strong software engineering or data engineering expertise, who has had exposure to contemporary Machine Learning practices and technologies.
The ideal candidate has experience with all parts of the MLOps lifecycle, including the registration, deployment, and monitoring of operation capabilities. It is expected that you will deliver key platforms and integrations to deliver self-service abilities to Data Scientists, to achieve continuous integration, continuous deployment, continuous training, and continuous monitoring. (LH-03971)
Role Description
The Machine Learning Engineer will perform the following duties and responsibilities:
- Design, develop, and maintain production MLOps platforms specific to ASD.
- Deploy, monitor, and troubleshoot ML models in production environments.
- Design and implement MLOps pipelines for deploying ML models to production.
- Review and optimise production ML code.
- Work with open-source technology and modern computing infrastructure.
- Work with other engineers to ensure successful integration into enterprise software.
- Work with data scientists to ensure that ML models are well tested and reliable.
Desirable:
Experience in one or more of the following areas:
- Software development with Python.
- Experience with MLOps tooling such as MLFlow, Ray, KubeFlow, Kserve or an enterprise ML platform.
- Building DevOps pipelines with GitLab CI/CD or equivilent.
- Kubernetes, Docker.
- Data engineering and data wrangling.
- Optimisation of model for production inference.
Essential criteria
- DENG 4 - Designs, implements and maintains complex data engineering solutions to acquire and prepare data. Creates and maintains data pipelines to connect data across stores, applications and organisations. Builds in compliance with data governance and security standards. Supports the development of continuous integration and deployment practices. Monitors and optimises pipeline performance and scalability. Conducts complex data quality checking and remediation. Leads data migration and data conversion activities.
- MLNG 5 - Leads the development and implementation of machine learning solutions for complex, high-impact business problems. Architects end-to-end machine learning pipelines and systems, incorporating MLOps practices. Evaluates and selects tools, frameworks and infrastructure for machine learning projects. Establishes practices and standards for machine learning development and operations. Provides expert advice and guidance on machine learning techniques and applications. Collaborates with stakeholders to align machine learning initiatives with organisational goals.
- RELM 4 - Plans and schedules releases in line with business requirements and objectives. Coordinates release activities across multiple teams and stakeholders. Manages the release lifecycle, ensuring timely and quality deliverables. Ensures releases meet defined quality, security and compliance standards. Communicates release plans, progress and outcomes to stakeholders. Conducts post-release reviews and identifies areas for improvement.
- SINT 4 - Provides technical expertise to enable the configuration of system components and equipment for systems testing. Collaborates with technical teams to develop and agree system integration plans and report on progress. Defines complex/new integration builds. Ensures integration test environments are correctly configured. Designs, performs and reports results of tests of the integration build. Identifies and documents system integration components for recording in the configuration management system. Recommends and implements improvements to processes and tools.