Skip to content
Machine Learning Engineer
| Company | Manulife Financial |
|---|
| Location | Toronto, ON, Canada |
|---|
| Salary | $75880 – $140920 |
|---|
| Type | Full-Time |
|---|
| Degrees | |
|---|
| Experience Level | Mid Level, Senior |
|---|
Requirements
- Hands-on experience with large-scale systems in software engineering.
- Experience in operationalizing 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 programming skills in Python and experience with ML libraries such as TensorFlow, PyTorch, or scikit-learn.
- 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.
- Experience with Large Language Models (LLMs) such as GPT-3 or BERT.
- An ability to balance a sense of urgency with shipping high quality and pragmatic solutions.
- Expertise in delivering analytics & machine learning products, with a deep understanding of agile product delivery in an enterprise environment.
Responsibilities
- Collaborate with Data Scientists and Data Engineers to design and implement scalable and efficient machine learning pipelines.
- Evaluate and optimize machine learning models for performance and scalability.
- Deploy machine learning models into production and monitor their performance.
- Handle data science infrastructure to streamline model development and deployment.
- Proposing appropriate tools (languages/libraries/frameworks) for implementing projects.
- Working closely with infrastructure architects to craft scalable and efficient solutions.
- Work closely with multi-functional teams to integrate machine learning models into existing systems and processes.
- Stay up-to-date with the latest advancements in ML & AI.
- Mentor associates and peers on MLOps standard practices.
Preferred Qualifications
No preferred qualifications provided.
