Senior Machine Learning Engineer

Key Skills

Python Programming, SQL, DevOps, Git, Cloud computing, REST API, Jenkins, NoSQL, Docker, Kubernetes-K8s, Terraform, Linux Operating system, PaaS

Job Description

The candidate will be a part of the Group Advanced Analytics team and will be tasked with packaging models, automating workflow, and operationalizing analytics solutions to generate business value to various parts of the JH/Manulife. The ideal candidate must be an advanced programmer with a good understanding of Data Science, Data Engineering, Application development, Software Engineering, and DevOps.

Typical Qualification:

• Bachelor’s degree in Computer Science, Data Science, Applied Math, Engineering, Operation Research, Statistics, or other related quantitative fields
• MS or Ph.D. preferred

Key Accountabilities:

• Brings a unique skillset that overlaps with Data Science, Data Engineering, Application Development, Software Engineering and DevOps
• Has a clear understanding of how to convert a model into a user-friendly application
• Manage data science infrastructure to streamline model development and deployment
• Architecting, developing, optimizing, and owning the deployment of ML functionalities into production systems
• Design and evaluate new approaches for handling high-volume real-time data streams in an inferencing environment
• Defining best practices for code, APIs, and frameworks that will lay the foundation for MLOps capabilities. Apply state of the art from research and practice of ML to Manulife's products
• Proposing appropriate tools (languages/libraries/frameworks) for implementing projects
• Working closely with Infrastructure architects to design scalable and cost-effective solutions
• Following Software Deployment and QA processes and MLOps best practices
• Develop measurements and feedback systems
• Model testing, production and deployment and Continuous integration/continuous delivery
• Work as part of a team with frequent interactions with data scientists. Daily activities may also require close touchpoints with architecture, cloud, data engineering, and risk management teams
• Mentor associates and peers on DevOps/MLOps best practices

Key Experiences:

• At least 5 years of hands-on experience with large-scale systems in software engineering
• Minimum 3 year of experience in productionizing code through DevOps pipeline (git, Jenkins pipeline, code scan)
• Familiarity with big data processing and building data APIs. Experience with automated data quality frameworks is a plus.
• Working experience in building and deploying machine learning models as REST-based API using Flask, Elasticsearch, etc.
• Strong software development skills (+3 years working experience) with proficiency in Python
• Advanced working SQL knowledge and experience working with relational databases and SQL
• Experience in infrastructure, including Cloud Computing, Linux OS, Networks, Docker, Kubernetes, RDBMS and NoSQL Databases
• Experience working with cloud native architecture (PaaS) using Azure stack preferably and experience with Azure ML, DataBricks (Spark), Azure Data Factory will be an asset.
• Experience in building ETL pipelines to perform feature engineering on large-scale dataset using Spark
• An ability to balance a sense of urgency with shipping high quality and pragmatic solutions
• Experience with user interface design (UX, PowerBI, node.js) and iterative customer-driven design processes would be an asset.
• Experience with Infrastructure as code (Terraform/ARM) is an asset
• Expertise delivering analytics & machine learning products, with a deep understanding of agile product delivery in an enterprise environment