Senior AI Engineer

At Omegro, we believe that the strength of our portfolio is a direct reflection of our people. As part of a global ecosystem (CSU:TSX), we offer the stability of a permanent home combined with the agility of a decentralized network. We are looking for experts who are passionate about operational excellence and eager to contribute to a culture that prioritizes professional development, flexible collaboration, and the shared success of our global network of businesses.

Position Overview:

The Senior AI Engineer is the Omegro AI Centre of Excellence's production engineering capability. Where AI Practitioners validate what is possible through rapid prototyping, the Senior AI Engineer builds what is proven — turning validated prototypes into robust, secure, and maintained Omegro-owned solutions. This role owns the production development and ongoing operation of Omegro-level AI products including M&A deal analysis agents, financial analysis tools, and the EAM Small Language Model (SLM). It also sets the technical architecture standards that BUs reference when building their own solutions and ensures that the CoE's growing portfolio of solutions remains secure, scalable, and maintainable over the long term.

Duties & Responsibilities:

Production Development — Omegro-Level Solutions

• Build, deploy, and maintain production-grade AI solutions for Omegro functions: M&A deal analysis and due diligence agents, financial analysis and reporting tools integrating with NetSuite and Adaptive Insights, knowledge management AI working alongside the Knowledge Management function, etc.
• Own the full development lifecycle for all Omegro-owned solutions — design, build, test, deploy, monitor, and iterate — ensuring solutions are reliable, secure, and fit for purpose.
• Lead technical development of the EAM Small Language Model (SLM) and associated data infrastructure as this strategic initiative matures, leveraging the portfolio's aggregated EAM data assets.
• Work with AI Practitioners to take validated prototypes through to production, ensuring continuity of intent while applying production-grade engineering standards.

Technical Architecture & Standards

• Define and maintain the technical architecture standards and reference patterns that govern how the CoE and, where appropriate, BUs design and build AI solutions across the portfolio.
• Create reusable SDKs, APIs, and shared services that BU engineering teams can adopt to accelerate their own development and avoid duplication.
• Conduct technical design reviews for significant cross-BU collaborative solutions, ensuring architectural integrity and long-term maintainability.
• Ensure security, data privacy, compliance, and auditability are built into all CoE solutions from the outset — not retrofitted.

Infrastructure & Operations

• Design and manage the shared AI infrastructure underpinning the CoE's solution portfolio: vector databases, model management, API gateways, monitoring, and observability tooling.
• Implement CI/CD pipelines, logging, and alerting for all production Omegro AI solutions.
• Define Service Level Objectives (SLOs) and monitor production performance, cost, and latency across the CoE's solution portfolio.
• Manage cloud infrastructure for Omegro AI solutions on Azure. Requires mastery across the full Azure stack for production AI: compute and app runtimes (App Service, AKS, Azure Functions), storage (Blob Storage, Cosmos DB, Azure AI Search), security and IAM (Entra ID, Key Vault, Managed Identities), and AI services (Azure OpenAI Service, Azure AI Studio).

Technical Leadership

• Act as the technical authority for the CoE — the primary escalation point for complex architecture decisions and production issues.
• Mentor AI Practitioners on production engineering practices, system design, and the transition from prototype to production.
• Evaluate and recommend technology choices for new initiatives, balancing innovation with practical maintainability.
• Contribute to the AI Playbook with technical architecture standards, decision records, and engineering best practices for BU reference.

Required skills and experience:

• 6–10 years of professional software engineering experience, with a strong recent focus on building and operating AI systems in production. You should be able to point to 2–3 AI/ML systems you have personally built and shipped at scale, with measurable business impact.
• Production-grade experience with LLM-based applications: RAG pipelines over large unstructured data sets, AI agents, function calling, custom MCP servers and skills, and evaluation frameworks. Practical examples include RAG-driven knowledge bases, productivity-focused skills (e.g. front-end design acceleration, brownfield discovery and documentation, bridging to legacy monoliths), or domain-tuned small language models.
• Strong backend engineering: Python and/or TypeScript, RESTful APIs, and data pipelines. Azure is the preferred cloud platform; Anthropic is the preferred LLM provider. Familiarity with both is strongly preferred.
• MLOps experience: model deployment, versioning, monitoring, cost and latency optimisation, and evaluation frameworks.
• Security-minded engineering: data privacy, PII handling, authentication and authorisation, audit trails, and compliance considerations.
• Experience integrating AI solutions with enterprise systems — ERP, CRM, financial platforms, and legacy systems via APIs and webhooks.
• Demonstrated ability to take prototypes to production and maintain them reliably — bias toward shipping and iterating over perfecting before release.
• Strong technical communication: able to define architecture standards clearly, write decision records, and explain technical trade-offs to both engineering peers and non-technical stakeholders.
• Experience mentoring more junior engineers or AI practitioners is desirable. Azure certifications (AZ-900, AI-102, AZ-305) are a strong asset.

Requirements

• The role can be performed via hybrid or remote in Toronto (or greater surrounding area) requiring you to have the right to work, and additionally the ability to travel domestically and internationally.

Values

These values are important to us in the Omegro Portfolio:

- Humble:
At Omegro, we define success collectively and work without ego. We share credit and put the company and team before ourselves. We believe in being learn-it-alls rather than know-it-alls, and we avoid any form of arrogance or ego-driven behaviour.
- Hungry:
We are tenacious and personally accountable, always playing to win and learn. We believe in hard work and continuous improvement, constantly seeking more and striving for excellence. Our intrinsic motivation drives us to achieve high standards and always aim for better.
- Smart:
We are great listeners and have the right people on the bus. We embrace a growth mindset, ask questions, read the room, and view feedback as a gift. We make data-driven decisions and are self-aware, ensuring that we can look back and be proud of our actions.

We welcome applicants with a wide range of experiences and perspectives. We hire based on qualifications and job-related criteria and do not discriminate on legally protected grounds. Accommodations are available throughout the recruitment process upon request.

As part of our commitment to transparency, we use artificial intelligence (AI) tools to assist in various stages of our recruitment process. These tools are designed to improve efficiency, reduce bias, and enhance candidate experience. All decisions regarding hiring are made by qualified human professionals, and we continuously monitor our AI systems to ensure fairness and compliance with applicable regulations.

The total remuneration package for this role is circa $170K - $185K.

The salary range provided is intended as a general guideline. Final compensation will be determined based on the successful candidate’s skills, experience, and alignment with the role.

If this role sounds well aligned with your experience and values, please apply through LinkedIn.