About the Role
We are assembling a specialist team focused on AI-driven workflow transformation, and we are looking for a hands-on Agentic AI Engineer to help lead it. This is a software engineering role not a research or model-training position. You will build production agents on top of foundation models, not train models from scratch.
You will design, build, and operate AI agents capable of autonomous decision-making, planning, and tool use to execute complex customer and internal workflows. The work spans agentic AI engineering, full-stack development, and AI-assisted delivery - applied to real product problems in an enterprise environment where reliability, security, and performance are non-negotiable.
This role is for someone who writes the code and leads the work - architecting a solution and shaping technical direction in the morning, and shipping a reliable, observable agent into production in the afternoon.
What You'll Do
- Design & Build Agentic Systems
- Design, build, and manage AI agents that plan, reason, make autonomous decisions, and use tools to execute multi-step customer and internal workflows.
- Implement agent orchestration, multi-step planning, and tool / function calling that connect LLMs to backend services, APIs, and enterprise data systems.
- Build LLM-powered and agentic applications using structured outputs, RAG (retrieval-augmented generation), memory, and state management.
- Contribute reusable internal patterns and components - prompts, tools, and agent scaffolding - so agentic features can be built consistently across the product.
- Reliability, Guardrails & Production Readiness
- Build guardrails, validation, and human-in-the-loop controls that keep agent behaviour reliable, safe, and auditable.
- Exercise sound judgment on when an agent should act autonomously versus when deterministic logic, validation, or human approval should drive behaviour.
- Support production readiness through strong engineering practice: observability, logging, debugging, CI/CD, and operational reliability.
- Monitoring & performance: instrument agentic systems end-to-end - measuring task success, latency, and cost - and build the dashboards that surface flexibility, reliability, and performance for enterprise stakeholders.
- Diagnose, optimise, and monitor AI solutions in production, driving issues to closure with a quality-first, secure-by-design mindset.
Collaboration & Technical Leadership
- Lead delivery across services and product surfaces, integrating agentic capabilities into production systems in a maintainable way.
- Partner with engineering, product, and design to define agent behaviours, evaluate solutions, and ship user-facing improvements.
- Improve quality through evaluation, testing, experimentation, and iteration; establish repeatable reliability practices for LLM applications.
- Document solutions, reference architectures, and runbooks that enable long-term maintainability and clean operational handoff.
What You'll Bring
- 7+ years of professional software engineering experience building and shipping production applications.
- 3+ years of hands-on experience building LLM-powered or agentic applications (tool / function calling, structured outputs, RAG, or multi-step agent workflows).
- Proven enterprise delivery: has built and shipped production-grade LLM / agentic implementations in an enterprise environment - end-to-end, from framing through deployment and monitoring.
- Writes code and leads: a genuine hands-on builder who also takes technical ownership - guiding design decisions, reviewing code, and setting the standard for the work.
Technical Skills
- Python - strong, production-grade coding ability with solid backend engineering fundamentals.
- LLM & agentic applications - hands-on experience designing prompts, tool use, structured outputs, and multi-step agent workflows on foundation models (e.g. Anthropic Claude, OpenAI / Azure OpenAI).
- Microsoft Azure - building and operating AI solutions in the Azure ecosystem (e.g. Azure OpenAI / Azure AI services) as the primary cloud platform.
- RAG - designing retrieval pipelines: indexing, embeddings, vector stores, and retrieval / contextualisation patterns.
- Containerisation & orchestration - hands-on with Docker and Kubernetes for packaging and running services reliably.
- C# or TypeScript / React - competence in at least one, with the ability to work across application layers and modern web surfaces.
- API-driven systems - building and integrating REST / GraphQL APIs and microservices, working across structured and unstructured data.
- Engineering Judgment & Ways of Working
- Able to turn ambiguous product or engineering requirements into structured, deliverable implementation plans.
- Strong grasp of core agent design patterns: planning, memory and state, tool use, and error handling and recovery.
- Attention to quality and a builder mindset - comfortable with code reviews, testing, and continuous improvement.
- Clear written and verbal communication; can explain AI concepts to both engineers and non-technical stakeholders.
Tools & Platforms You'll Work With
Our agentic stack is Microsoft-centric. You will design, build, and operate across the full path from user query to governed tool execution:
- Azure AI Foundry - our core platform for model orchestration, agent runtime, deployment, and governance (Foundry prompt agents and hosted agents).
- Microsoft Foundry Agent Service - the orchestration layer that routes requests to approved tools, data agents, and workflows.
- MCP (Model Context Protocol) servers - designing and exposing reusable custom MCP servers and connectors that securely link agents to approved tools and enterprise data sources.
- Microsoft 365 & Teams / M365 Copilot - integrating agentic capabilities into the surfaces where users actually work (Teams actions, Outlook, calendar, SharePoint).
- Custom tool layer - defining and governing the approved tools an agent can call (Microsoft 365, Power BI / Lakehouse data, quality records and sign-offs).
- Observability stack - OpenTelemetry and Azure Monitor for advanced agent traceability, tool-call monitoring, and reliability; M365 Admin Centre, Power BI Copilot dashboards, and Viva Insights for usage and adoption visibility.
Nice to Have
- Has used AI coding tools to meaningfully change how they build software - not just experimented with them.
- Experience shipping AI-powered product features in enterprise or regulated software (e.g. financial services).
- Familiarity with LLMOps / MLOps / GenAIOps: environment management, evaluation, red-teaming, and incident response for AI deployments.
- Exposure to MCP (Model Context Protocol) servers, connectors, and reusable integration standards.
- Interest in cybersecurity, automation, or workflow-heavy systems.
How to Apply
Please include the following with your application:
- A summary of relevant experience building and shipping LLM-powered or agentic applications.
- Examples of agents, agentic workflows, prompts, or AI features you have personally designed and built - deployed examples strongly preferred over proofs-of-concept.
- The tools, platforms, and languages you work with regularly (Python, Azure, RAG stack, Docker/Kubernetes, C#/TypeScript/React).
- Any demos, repositories, or production examples you are able to share.
Job Type: Fixed term contract
Pay: $85,297.89-$145,000.00 per year
Work Location: Hybrid remote in Toronto, ON (Toronto District)
