AI/ML Engineer

Why Join Us?

This role will join the AI Delivery and Innovation team within Platform Engineering, AI and Advanced Analytics department accelerating how OMERS prototypes, builds, and ships AI solutions across the enterprise.

As a key member of the team, you will be hands-on across the full AI delivery lifecycle — from rapid prototyping and proof-of-concept development through to production-ready deployment. The scope spans applied machine learning, Generative AI and LLM-based applications, MLOps, and full-stack engineering, building both the backend services and the user-facing experiences that bring AI capabilities to internal customers.

As a member of this team, you will be responsible for:

Technical

• Designing and building end-to-end AI/ML and Generative AI solutions, including LLM applications, RAG pipelines, agentic workflows, and traditional ML models
• Implementing prompt engineering, fine-tuning, evaluation harnesses, and guardrails for LLM-based applications
• Building and maintaining MLOps/LLMOps/GenAIOps pipelines covering experiment tracking, model and prompt versioning, CI/CD, observability, drift detection, and automated retraining
• Integrating with foundation models hosted on Azure AI Foundry, Copilot Studio, and other approved enterprise AI platforms
• Working with vector databases, embeddings, and retrieval systems to ground LLMs on enterprise knowledge
• Containerizing workloads (Docker, Container Applications) and deploying across cloud and on-premises GPU infrastructure
• Conducting applied research on emerging models, agent frameworks, and AI engineering patterns to inform team direction
• Writing clean, tested, production-quality code with appropriate documentation and instrumentation
• Developing full-stack applications that operationalize AI capabilities for business users

Organizational

• Operating in an Agile development environment alongside business partners
• Communicating technical concepts and trade-offs clearly to non-technical stakeholders, project managers, and members of senior management
• Working both in a fast paced innovation and production based environment
• Identifying, defining and implementing opportunities for improving existing engineering practices and tooling
• Working on multiple initiatives simultaneously and ensuring timely delivery

To succeed in this role, you have:

Professional

• Experience. 6+ years of software engineering experience, including 3+ years building and deploying production AI/ML or Generative AI solutions
• A Bachelor’s Degree in Computer Science, Engineering, Mathematics, or a related quantitative field, or equivalent work experience. Master’s Degree is considered as an asset
• Industry Knowledge of modern AI engineering patterns, including RAG, agentic workflows, evaluation, and responsible AI. Financial services experience is considered an asset.
• Demonstrated success delivering complex technical projects end-to-end and aligning expectations across various partners
• Problem-Solving. Comfortable navigating ambiguity, translating fuzzy business problems into technical solutions, and rapidly iterating from prototype to production with minimal direction
• Teamwork. Motivated and keen to work in a collaborative environment with a focus on team success, fast feedback, and shared ownership of outcomes

Technical

• AI/ML and GenAI. Hands-on experience with LLMs (OpenAI, Anthropic, open-source), prompt engineering, RAG architectures, fine-tuning, and frameworks such as LangChain, LlamaIndex, or Semantic Kernel. Exposure to agent frameworks (e.g., Microsoft Agent Framework, Google ADK) is a plus.
• Machine Learning. Solid foundation in classical ML (scikit-learn, XGBoost) and deep learning (PyTorch, TensorFlow), including feature engineering, model evaluation, and experimentation
• MLOps/LLMOps. Experience with MLflow, Kubeflow, or equivalent tools; familiarity with model registries, CI/CD for ML, observability (Arize, Langfuse, or similar), and drift monitoring
• Full-Stack Engineering. Experience in developing FS applications that connect with AI solutions
• Cloud & Infrastructure. Working knowledge of Azure (preferred), including Azure AI Foundry / Azure OpenAI
• Data. SQL fluency and experience working with modern data platforms (Databricks, Snowflake) and vector databases (Azure AI Search, Pinecone, pgvector, or similar)
• Software Engineering Practices. Strong Git, code review, automated testing, and CI/CD habits, with a bias toward shipping reliable software.
• Breadth of technical experience and knowledge in the AI/ML ecosystem, with depth in two or more of the following: LLM application development, classical ML model delivery, MLOps/LLMOps tooling, full-stack web engineering, or AI infrastructure