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Toronto, Ontario, Canada
Hybrid
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.
