Job Description
- Design and build a reliable, secure and scalable machine learning platform that allows our data scientists to bring AI/ML models to production quickly and responsibly.
- Responsible for optimizing hyper parameters in a model, and evaluating those models.
- Take offline models data scientists build and turn them into a real machine learning production system.
- Identify business and technical requirements to define the platform solution, working with data science, product, cloud/release and other technical teams.
- Partner with stakeholders as a subject matter expert on ML infrastructure at scale.
- Ensure delivery of high quality engineering solutions with the team, establishing and implementing best practices for development, testing and deployment such as Infrastructure-as-Code, GitOps, container orchestration.
- Implement agile software development principles, DevOps and MLOps practices to drive a bigger impact for the team and data science users.
- Identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems.
- Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
- Create and apply standards, metrics, and monitoring to gauge and enhance service quality.
- Support model development, with an emphasis on auditability, versioning, and data security.
- Facilitate the development and deployment of proof-of-concept machine learning systems.
Key Stakeholder Management
Internal
- M&G Plc.
- M&G Global Services
External
Knowledge, Skills, Experience & Educational Qualification
Knowledge & Skills:
- Good programming skills, hands-on experience with machine learning frameworks, libraries, agile settings, and implementing machine learning solutions utilizing DevOps concepts.
- Ability to design and implement cloud solutions (MS Azure)
- Ability to build MLOps pipelines on cloud solutions (MS Azure)
- CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Circle CI, Airflow or similar tools.
- Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc.
- Experience with Docker and Kubernetes.
- Programming language - PySpark, Python.
- Exposure to machine learning methodology and best practices
- Knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc. and ability to understand tools used by data scientist.
- Familiar with data structures, data modelling, and database management systems.
- Experience with software development and test automation.
- Good communication skills and ability to work in a team.
Good to have
- Azure Synapse knowledge.
- Knowledge and exposure to Regulatory processes like Information Security, Data Protection, etc.).
- Onshore/Client site working experience.
Experience:
- At least 7 years of relevant experience.
Educational Qualification:
- Preferably should have Technical/Science Graduation degree with specialization in Computer Science, Statistics, Mathematics, Science, Engineering or related discipline
- Data, Analytics and Business Intelligence certification would be an add on
M&G Behaviours relevant to all roles:
Inspire Others: support and encourage each other, creating an environment where everyone can contribute and succeed
Embrace Change: be open to change, willing to be challenged and able to adapt quickly and imaginatively to new ideas
Deliver Results: focus on performance, set high standards and deliver with energy and determination
Keep it simple: cut through complexity, keep the outcome in mind, keeping your approach simple and adapting your message to every audience