CorGTA Inc. hiring Machine Learning Engineer (Vertex AI) in Canada

*****This company is a Data Consultancy & Solution Provider with high-profile clients that works with Data Scientists, Data Engineers, and Software Engineers to solve any & all business problems. (Data & Analytics Strategy, Data Governance, Data Warehousing, Predictive Analytics, & Cloud BI Services)

Machine Learning Engineer (Vertex AI)

Job Title: Machine Learning Engineer (Vertex AI)

Location: Anywhere in Canada

Salary: up to $170,000 CAD + 10 % bonus + benefits

Working Structure: Remote

What you bring to this role:

– 4+ years of software development experience, preferably in Python.

– Experience with AI-Ops concepts and practices. Have worked or knowledge of LLMs, Multimodal and Gen AI implementation.

– Experience with maintaining functional, production reference architectures for end-to-end Machine Learning in the cloud.

– Experience with Vertex AI is a must have

– Must have previous experience with ML-Ops tools and platforms such as Vertex AI, MLFlow/Airflow and DVC.

– Need to have strong Linux system administration skills.

– Experience with Kubernetes (GKE) for model serving and scalable inference.

– Exposure to automated testing and CI/CD in the ML context.

– Knowledge of SQL and relational databases, query authoring (SQL)

– Experience designing a variety of databases (e.g., Postgres SQL).

– Strong communication and collaboration skills.

What you will do in this role:

– Design and implement large-scale ML systems to support training and serving workloads.

– Collaborating and share knowledge with our cloud ops team to compress time-to-production for Machine Learning.

– Build tooling and pipelining abstractions to allow Data scientists to focus on experimentation while empowering self-service workflows to deploy and serve models reliably and consistently.

– Help Data Scientists produce clean, reproducible, and highly performant machine learning systems through rigorous code review with a lens on software quality.

– Advocate for automation and monitoring at all steps of ML system construction, and help to define best practices based on personal industry experience and research across the Machine Learning team.

– Support life cycle management of deployed ML apps (e.g., new releases, change management, monitoring and troubleshooting).

– Participate in sprint planning, estimations, and reviews.

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