Lead, AI/Machine Learning Engineer
Ontario Municipal Employees Retirement System2 months ago
Toronto, CanadaStaff+
Responsibilities
- Design and build end-to-end AI/ML and Generative AI solutions, including LLM applications, RAG pipelines, agentic workflows, and traditional ML models.
- Build and maintain MLOps, LLMOps, and GenAIOps pipelines covering experiment tracking, model and prompt versioning, observability, drift detection, and automated retraining.
- Develop AI solutions using enterprise platforms including Azure AI Foundry and Copilot Studio.
- Implement vector database, embedding, and retrieval systems to ground LLMs in enterprise knowledge.
- Conduct applied research on emerging models, agent frameworks, and AI engineering patterns and translate findings into reusable solutions.
- Collaborate with software engineering, customer success, and business stakeholders to move initiatives from prototype to production.
- Contribute to AI governance, responsible AI controls, architecture standards, and reusable skill, sub-agent, and component libraries.
- Mentor and coach teammates through pairing, code reviews, and knowledge sharing.
- Improve engineering practices, tooling, and delivery processes while managing multiple initiatives and ensuring timely delivery.
Requirements
- 3+ years of professional software engineering experience, including 2+ years building and deploying production AI/ML or Generative AI solutions.
- Hands-on experience with LLMs including OpenAI, Anthropic, and open-source models, along with prompt engineering, RAG architectures, and fine-tuning.
- Experience with LLM/GenAI frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
- Strong machine learning foundations covering classical ML, deep learning, feature engineering, model evaluation, and experimentation.
- Experience implementing MLOps/LLMOps capabilities, model registries, ML CI/CD, observability, and drift monitoring.
- Ability to design, build, and maintain production-grade services and full-stack applications integrating AI capabilities.
- Strong Azure experience, including Azure AI Foundry and Azure OpenAI; GCP and Vertex AI experience is an asset.
- Strong SQL skills and experience with Databricks, Snowflake, and vector databases such as Azure AI Search, Pinecone, or pgvector.
- Experience delivering complex technical projects end-to-end and navigating ambiguity from prototype to production.
- Strong software engineering practices including Git, code reviews, automated testing, and reliable maintainable software delivery.
- Excellent communication skills with the ability to explain technical concepts and trade-offs to non-technical stakeholders and senior management.
- Preferred experience with Microsoft Agent Framework, Google ADK, agentic workflows, Docker, Container Applications, GPU infrastructure, AI evaluation tooling, LLM guardrails, responsible AI, governance, and enterprise architecture.
- Experience in financial services, pensions, asset management, or related domains is preferred.
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related quantitative field, with a master’s degree an asset, or equivalent work experience.
- Experience mentoring engineers and contributing to communities of practice or reusable component libraries.
Benefits
- Flexible hybrid work arrangement requiring teams to work in the office four days per week.
- Eligibility for an annual Incentive Award under the Short-term Incentive plan and Long-Term Incentive plan where applicable.
- Participation in group benefits and retirement plans.
- Inclusive, barrier-free recruitment and selection process with employee resource groups, Purpose@Work, and employee recognition programs.
Tech Stack
Categories