Applied Machine Learning Scientist developing Generative AI and predictive ML solutions at TD, a major North American bank. Supporting model evaluation, deployment, monitoring, and responsible AI governance.
EC
AI & Machine Learning Engineer, Level 17 or 18
Posted 3 weeks ago
About the role
- AI/ML Engineer building production ML and GenAI solutions for Export Development Canada. Applying MLOps, cloud, governance, and Responsible AI practices across enterprise systems.
Responsibilities
- Design and maintain reusable ML assets, including feature pipelines, shared components, deployment patterns, and evaluation frameworks
- Collaborate with data scientists, architects, platform, and security teams to transition models from research to scalable, reliable production services
- Engineer and deploy production-grade ML and GenAI solutions using batch, real-time, and event-driven inference patterns
- Own or support the AI system lifecycle, including MLOps, LLMOps, AgentOps, versioning, monitoring, retraining, scaling, rollback, and retirement
- Operationalize RAG-based and agentic GenAI applications with evaluation, guardrails, and cost awareness
- Embed security, governance, Responsible AI controls, Protected B requirements, auditability, and risk-based controls
- Automate AI delivery through CI/CD pipelines, Infrastructure as Code, and standardized environment promotion
- Monitor, diagnose, and remediate system health, model and data drift, bias indicators, cost anomalies, and production incidents within SLAs
- Build and validate predictive, descriptive, behavioural, and structured-data machine learning models
- Partner with business stakeholders and SMEs to translate insights into actionable recommendations
- Apply engineering judgment to balance performance, scalability, cost, security, and risk
- Provide fault isolation, initial resolution, concepts, and prototypes for AI product and service ideas
Requirements
- University degree in Computer Science, Engineering, Mathematics, Data Science, or a related technical discipline
- Level 17: Minimum 5 years of experience in AI and ML engineering roles delivering production systems in enterprise environments
- Level 17: Minimum 5 years of experience designing or contributing to large-scale data platforms supporting batch and real-time workloads, primarily using Databricks and Azure
- Level 17: Minimum 3 years of hands-on experience building and operating ML or AI systems in production, including monitoring, retraining, and incident response
- Level 17: Minimum 3 years of experience with Azure cloud deployment, automation, networking, and security services, with focus on Databricks data operations
- Level 17: Minimum 3 years of experience implementing CI/CD pipelines and Infrastructure as Code for ML/AI workloads
- Level 17: Minimum 3 years of experience developing production-grade code using Python and data-centric languages such as SQL, Java, or Scala
- Level 17: Minimum 3 years of experience in formal IT service management and Agile delivery environments
- Level 17: Minimum 1 year of applied GenAI or MLOps experience, including LLMs, RAG-based architectures, or agentic/workflow-oriented patterns in production
- Level 18: Minimum 7 years of experience in AI and ML engineering roles delivering production systems in enterprise environments
- Level 18: Minimum 7 years of experience designing or contributing to large-scale data platforms supporting batch and real-time workloads, primarily using Databricks and Azure
- Level 18: Minimum 7 years of hands-on experience building and operating ML or AI systems in production
- Level 18: Minimum 7 years of experience with Azure cloud deployment, automation, networking, and security services
- Level 18: Minimum 7 years of experience implementing CI/CD pipelines and Infrastructure as Code for ML/AI workloads
- Level 18: Minimum 5 years of experience developing production-grade code using Python and data-centric languages
- Level 18: Minimum 7 years of experience in formal IT service management and Agile delivery environments
- Level 18: Minimum 3 years of applied GenAI or MLOps experience in production
- Candidates must be able to work legally in Canada at the time of application
- Candidates must meet government security screening requirements
Benefits
- Performance-based incentive
- Competitive compensation and benefits package
- 3 to 4 weeks paid vacation
- Corporate closure period
- Summer early Fridays
- No meeting Fridays
- Hybrid work options
- Relocation assistance for eligible candidates
- Continuous learning opportunities
- Training programs and workshops
- Language training
- Wellness initiatives
- Mental health support
- Fitness programs
- Volunteer opportunities
- Social responsibility programs
Job title
Job type
Experience level
Salary
Degree requirement
Tech skills
Location requirements
Report this job
Found something wrong with the page? Please let us know by submitting a report below.
Applied Machine Learning Scientist developing Generative AI and predictive ML solutions for TD banking. Evaluating models, managing AI lifecycles, and supporting responsible implementation.
Senior Machine Learning Engineer building conversational AI agents and production ML systems. Helping Numa automate automotive dealership service and sales through evaluation - first tooling and infrastructure.
Senior ML Engineer building production ML, RAG, and agentic AI capabilities for SailPoint’s cloud identity security platform. Driving scalable, customer - focused AI solutions from research to production.
Senior ML Engineer developing debiased pCTR and conversion models for Instacart’s grocery advertising ecosystem. Advancing ranking, retrieval, and sequence modeling across ads surfaces.
Senior AI/ML Engineer building Generative AI, RAG, and agentic solutions for pharmaceutical Statistical Programming. Deploying secure, validated, production - ready AI applications with Python and AWS.
Senior Machine Learning Engineer building LLM - powered lab interpretation and clinical decision - support tools for Fullscript’s healthcare platform. Owning AI systems from prototyping through production.
Staff ML engineer building models, evaluations, and agentic systems for Sourcegraph’s code - understanding products. Improving enterprise code search quality, latency, cost, and reliability.
Senior Machine Learning Engineer productionizing computer vision models for Samsara’s IoT - powered Connected Operations Cloud. Building low - latency APIs, data pipelines, and monitoring for fleet - scale safety features.
Machine Learning Engineer supporting Helm.ai’s neural - network research operations. Evaluating experiments, identifying failure modes, and reporting quantitative results for autonomous - driving technology.