Lead, AI/Machine Learning Engineer

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.