Machine Learning Engineer
About The Position
Empire Life is looking to hire a Machine Learning Engineer to join our Data and Analytics team! We are actively seeking candidates to fill a current, open position. The Machine Learning Engineer plays a critical role within the AI and Data Science team, responsible for designing, building, deploying, and operationalizing scalable, production-grade machine learning and Generative AI (GenAI) solutions. Operating within a modern Microsoft Azure and Snowflake-based ecosystem, this role focuses on AI solution architecture, MLOps, robust ML pipelines, observability, and platform scalability. The Machine Learning Engineer bridges the gap between experimentation and enterprise delivery, collaborating closely with Data Scientists, software engineers, and cloud teams to enable secure, reliable AI capabilities—including LLM-based applications, Retrieval-Augmented Generation (RAG), intelligent document processing, and agentic AI workflows. This role reports to the Director of Data and AI strategy.
Requirements
- Master’s degree in Computer Science, Software Engineering, Electrical Engineering, Computer Engineering, or a related technical field
- 3+ years of working experience with hands-on industry experience designing, developing, and deploying machine learning or AI-enabled applications
- Strong understanding of cloud-native AI and data platforms, particularly within Microsoft Azure ecosystems and enterprise data environments such as Snowflake
- Strong software engineering and system design experience focused on scalable AI/ML applications, distributed systems, APIs, cloud-native services, or enterprise platform engineering
- Demonstrated experience building and operationalizing machine learning, Generative AI, or intelligent automation solutions in enterprise or production environments
- Strong programming skills in Python and SQL with experience building production-grade applications, automation workflows, APIs, and AI services
- Familiarity with AI evaluation frameworks, monitoring systems, observability tooling, and operational telemetry for AI applications.
- Familiarity with modern Generative AI ecosystems including LLM APIs, retrieval-augmented generation (RAG), vector databases, orchestration frameworks, prompt engineering, and agentic AI systems
- Understanding of MLOps, AIOps, observability, AI governance, model lifecycle management, and operational reliability practices
- Strong communication, collaboration, and problem-solving skills with the ability to work across technical and business teams
- Language proficiency in English is required for communicating with customers, advisors, or employees across Canada.
Nice To Haves
- Microsoft, Azure, Kubernetes, Snowflake, Databricks, or cloud engineering certifications are considered an asset
Responsibilities
- Collaborate with data scientists, software engineers, cloud infrastructure teams, and business stakeholders to design scalable AI and machine learning solutions aligned with enterprise requirements
- Design, develop, and optimize scalable AI/ML pipelines for data ingestion, feature processing, model training, evaluation, deployment, and monitoring
- Operationalize feature engineering, model serving, vector search, and inference workflows for production AI applications
- Deploy and maintain machine learning and Generative AI solutions in secure, scalable, and highly available production environments
- Implement testing, validation, observability, and monitoring frameworks to support reliability, performance, and governance of AI systems
- Partner with DevSecOps and cloud engineering teams to automate CI/CD workflows, infrastructure provisioning, model deployment, monitoring, and operational support for AI platforms
- Develop and support Generative AI systems including LLM integrations, retrieval-augmented generation (RAG), vector databases, orchestration frameworks, and intelligent agents
- Support MLOps, LLMOps, and emerging AIOps practices including model lifecycle management, deployment automation, drift monitoring, evaluation pipelines, logging, and operational governance.
Benefits
- Hybrid work model
- Competitive salaries with annual pay increases
- Annual bonus program, which recognizes both strong company performance and individual contributions, for non sales positions
- Access to learning & development programs, and education/tuition reimbursement (role dependent), to support your professional growth and career advancement.
- Competitive uncapped commission, for sales positions
- A comprehensive employer-funded benefits package starting from day one of employment, that includes life insurance, health and dental and a generous health account
- Flexible work arrangements and an annual allotment of personal health days.
- Four weeks annual vacation from hire date
- A defined contribution pension plan with generous employer matching
- Top up programs for parental leave and compassionate leave
- Employer-sponsored wellness and recognition programs
- A cash employee referral program
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
