Lead, AI/Machine Learning Engineer

Choose a workplace that empowers your impact. 

Join a global workplace where employees thrive. One that embraces diversity of thought, expertise and experience. A place where you can personalize your employee journey to be — and deliver — your best.  

We are a purpose-driven, dynamic and sustainable pension plan. An industry leading global investor with teams in Toronto to London, New York, Singapore, Sydney and other major cities across North America and Europe. We embody the values of our 665,000 members, placing their best interests at the heart of everything we do.

Join us to accelerate your growth & development, prioritize wellness, build connections, and support the communities where we live and work.

Don’t just work anywhere — come build tomorrow together with us.

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The Lead, AI/Machine Learning Engineer will join the AI Delivery and Innovation team within the Platform Engineering, AI and Advanced Analytics department. This team acts as a central hub for AI capability at OMERS, partnering with Software Engineering, Customer Success and Innovation (CSI), and business areas to prototype, build, and ship AI solutions across Investments, Pension Services, Finance, and Corporate functions.

Reporting to the Associate Director, AI and ML, this role is hands-on across the full AI delivery lifecycle — from rapid prototyping and proof-of-concept development through to production-ready deployment. You will design and implement AI/ML and Generative AI solutions, operationalize them through robust engineering practices, and help shape how OMERS leverages AI to deliver measurable business outcomes. This is an opportunity to work at the intersection of cutting-edge AI, modern engineering, and real-world business problems in a collaborative, fast-paced environment.

You will be responsible for:

• Designing and building end-to-end AI/ML and Generative AI solutions, including LLM applications, RAG pipelines, agentic workflows, and traditional ML models.

• Building and maintaining MLOps/LLMOps/GenAIOps pipelines, including experiment tracking, model and prompt versioning, CI/CD, observability, drift detection, and automated retraining.

• Building AI solutions using enterprise platforms, including Azure AI Foundry, Copilot Studio, and other approved AI platforms.

• Working with vector databases, embeddings, and retrieval systems to ground LLMs on OMERS enterprise knowledge.

• Conducting applied research on emerging models, agent frameworks, and AI engineering patterns, and translating findings into practical solutions and reusable components.

• Collaborating with Software Engineering, Customer Success, and business stakeholders in an Agile environment to move initiatives from prototype to production and ensure successful adoption.

• Contributing to AI governance, responsible AI practices, and architecture standards; embedding responsible AI principles and controls in everything you build.

• Mentoring and coaching teammates through pairing, code reviews, and knowledge sharing; contributing to reusable skill, sub-agent, and component libraries to accelerate delivery.

• Identifying, defining, and implementing improvements to existing engineering practices, tooling, and delivery processes while managing multiple initiatives and ensuring timely delivery.

Required Skills & Experience

• 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; prompt engineering; RAG architectures; and fine-tuning.

• Practical experience with one or more LLM/GenAI frameworks, such as LangChain, LlamaIndex, or Semantic Kernel.

• Strong foundation in machine learning, including classical ML, such as scikit-learn, and deep learning, such as PyTorch or TensorFlow, with experience in feature engineering, model evaluation, and experimentation.

• Experience implementing MLOps/LLMOps capabilities, including MLflow, Kubeflow, or equivalents; model registries; CI/CD for ML; observability, such as Arize, Langfuse, or similar; and drift monitoring.

• Proven ability to design, build, and maintain production-grade services and full-stack applications that integrate AI capabilities.

• Solid experience with cloud platforms, particularly Azure, including Azure AI Foundry and Azure OpenAI; working knowledge of GCP and Vertex AI is an asset.

• Strong SQL skills and experience working with modern data platforms, including Databricks and Snowflake, and vector databases, including Azure AI Search, Pinecone, pgvector, or similar.

• Demonstrated success delivering complex technical projects end-to-end, aligning expectations with various partners, and navigating ambiguity from prototype to production.

• Strong software engineering practices, including Git, code reviews, automated testing, and CI/CD, with a bias toward shipping reliable, maintainable software.

• Excellent communication skills, with the ability to explain technical concepts and trade-offs clearly to non-technical stakeholders and senior management.

• Motivated to work in a collaborative environment with fast feedback, shared ownership of outcomes, and a focus on team success.

Preferred Skills & Experience

• Experience with agent frameworks, such as Microsoft Agent Framework or Google ADK, and agentic workflows.

• Experience containerizing workloads, including Docker and Container Applications, and deploying across cloud and on-premises GPU infrastructure.

• Exposure to AI/ML observability and evaluation tooling beyond the core stack, and experience designing evaluation harnesses and guardrails for LLM-based applications.

• Familiarity with responsible AI principles, governance frameworks, and enterprise architecture standards.

• Experience in financial services, pensions, asset management, or related domains.

• Bachelor’s Degree in Computer Science, Engineering, Mathematics, or a related quantitative field; Master’s degree is an asset, or equivalent work experience.

• Experience mentoring engineers and contributing to communities of practice or reusable component libraries.

We believe that time together in the office is important for OMERS and Oxford, the strength of our employees, and the work we do for our pension members. In delivering on our pension promise, keeping us connected to our work and each other, our flexible hybrid work guideline requires teams to come in to the office 4 days per week. 

You may also be eligible to receive an annual Incentive Award pursuant to our Short-term Incentive plan and our Long-Term Incentive plan (if applicable), and to participate in our group benefits and retirement plans – details on these elements of compensation are included within OMERS & Oxford offer letters.

As one of Canada’s largest defined benefit pension plans, our people-first culture is at its best when our workforce reflects the communities where we live and work — and the members we proudly serve.

From hire to retire, we are an equal opportunity employer committed to an inclusive, barrier-free recruitment and selection process that extends all the way through your employee experience. This sense of belonging and connection is cultivated up, down and across our global organization thanks to our vast network of Employee Resource Groups with executive leader sponsorship, our Purpose@Work committee and employee recognition programs.

Artificial intelligence (AI) tools are used to support certain stages of the OMERS recruitment process. While AI assists us in our process, human judgment and decision-making remain central to our candidate experience.