Lead, AI​/Machine Learning Engineer

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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.

• 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.

• 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 Lang Chain, Llama Index, or Semantic Kernel.
• Strong foundation in machine learning, including classical ML, such as scikit‑learn, and deep learning, such as PyTorch or Tensor Flow, 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…