AI/ML Engineer
Vanguard Open · verified Oct 21, 2026 At a glance
Mid-level Machine Learning Engineering role at Vanguard. Toronto · full-time · no salary stated · we estimate CA$133,650–CA$197,920.
Vanguard has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build AI/ML systems from data intake and feature creation through training, launch, monitoring and ongoing updates
- Create cloud-based ML services on AWS and maintain pipelines for both scheduled and live workloads
- Develop generative AI applications using language models, retrieval methods, vector stores and agent frameworks
- Use knowledge graphs and graph databases to support enterprise analysis and decisions
- Turn business problems into analytical solutions, investigate data quality, and guide production oversight and governance
What you bring
- A relevant technical bachelor's degree and at least six years in machine learning, data or software engineering
- At least three years building scalable data pipelines and ETL processes with AWS services
- Strong Python skills, plus experience operating production AI/ML applications in cloud environments
- Hands-on work with SageMaker, MLOps, CI/CD, model deployment and monitoring, and lifecycle practices
- Experience with generative AI and knowledge graphs, including retrieval systems, vector databases and graph databases
Who this fits
This mid-level role suits an engineer who can work across software, data and machine learning, while leading technical discussions and mentoring teammates. The position uses a hybrid working model.
From the employer
The ideal candidate combines strong software engineering, machine learning, and cloud expertise with hands-on experience delivering production-grade AI solutions. This role will partner closely with business stakeholders, product teams, and engineers to solve complex business problems using machine learning, Generative AI, and advanced analytics.
Responsibilities
Design, develop, and deploy end-to-end AI/ML solutions, including data ingestion, feature engineering, model training, deployment, monitoring, and lifecycle management.
Build scalable, cloud-native AI/ML applications and services using AWS technologies such as SageMaker, ECS, Lambda, S3, EventBridge, and Step Functions.
Develop and maintain machine learning, data engineering, and MLOps pipelines supporting batch and real-time workloads.
Design and implement Generative AI solutions leveraging Large Language Models (LLMs), Advanced RAG, vector databases, knowledge retrieval systems, agentic AI frameworks, and fine-tuning techniques.
Design and utilize knowledge graphs, graph databases, and relationship-based analytics to enhance enterprise intelligence and decision-making.
Partner with business stakeholders to translate business challenges into scalable analytical and AI-driven solutions.
Conduct data discovery and exploratory analysis, establish data lineage, and perform root cause analysis to ensure data quality and reliability.
Implement model monitoring, observability, alerting, and operational support processes for production AI/ML solutions.
Ensure adherence to enterprise AI governance, security, Responsible AI, privacy, and model risk management standards.
Serve as a machine learning engineering subject matter expert, lead technical design discussions, and mentor team members on AI/ML best practices.
Stay current on emerging AI technologies and evaluate their application to business opportunities.
Qualifications
Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field; Master's degree preferred.
6+ years of experience in Machine Learning Engineering, Data Engineering, Software Engineering, or a related discipline.
3+ years of hands-on experience building scalable data pipelines and ETL solutions using AWS services.
Strong proficiency in Python and modern software engineering practices.
Experience deploying and supporting production-grade AI/ML applications in cloud environments, preferably AWS.
Strong experience with SageMaker, MLOps, CI/CD pipelines, model deployment, monitoring, and Machine Learning Development Lifecycle (MDLC) practices.
Experience with containerization and orchestration technologies such as Docker, ECS, and Kubernetes.
Experience with Generative AI technologies, including LLMs, Advanced RAG, vector databases, semantic search, agentic AI frameworks, and enterprise knowledge retrieval systems.
Experience designing and implementing knowledge graph solutions and graph databases.
Strong understanding of software engineering fundamentals, including system design, testing, security, observability, and version control.
Ability to lead technical initiatives, influence architectural decisions, and collaborate effectively across business and technology teams.
Preferred Experience
Real-time data processing and streaming technologies such as Kafka, Flink, or Kinesis.
AI governance, Responsible AI, and model risk management frameworks.
Enterprise-scale AI platform development and solution architecture.
How We Work
Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.