Staff Software Engineer - AI-native Development & Agentic Systems

Title: Staff Software Engineer - AI-native Development & Agentic Systems

Position Type Contract Location Toronto, ON Working model Flexible Hybrid work model - 1 to 3 days per week in office

Engagement Model: Professional Services Engagement – B2B (Incorporated Entities Only)

About the Engagement:

Akkodis is partnered with a global leading IT Service & Technology firm who specialize in AI and cloud-based data solutions across North America here in Toronto.

We’re looking for Senior Engineers with at least 10+ years of experience building and shipping production systems, who have a passion and proven hands-on expertise leveraging AI & Agentic systems to aid in the development and delivery of software engineering solutions and applications.

You’d be joining a new team who are looking to build a new delivery model centered around small teams, senior talent, high autonomy, and AI-accelerated execution, If successful, it has the potential to reshape how software is built across the organization.

Services to be Provided:

Own Delivery End-to-End

• Take client problems from scoping through architecture, build, deployment, and iteration. You’re not waiting for a spec.
• Deliver production-grade applications across the stack: React/TypeScript frontends, Node.js or .NET APIs, Azure cloud services, data pipelines on Databricks.
• Use AI-assisted workflows (Claude Code, Copilot, Cursor, whatever works) to compress timelines. The expectation isn’t that you use specific tools—it’s that you ship fast without sacrificing quality.

Design & Build Agentic Systems

• Architect and build autonomous agent systems that can reason, plan, use tools, and take actions in real-world workflows—not just chatbot wrappers.
• Design agent orchestration patterns: multi-agent coordination, human-in-the-loop checkpoints, tool use, memory, and context management.
• Build the infrastructure that supports agentic deployment: evaluation frameworks, guardrails, monitoring, and observability for systems that act on their own.
• Stay close to the frontier. The tooling is moving fast (LangGraph, CrewAI, Autogen, custom orchestration). You should have opinions about what works and what doesn’t.

Multiply the Team

• Build reusable accelerators—deployment pipelines, security patterns, API scaffolding, agent frameworks—that compound across engagements.
• Raise the engineering bar on projects you touch. Code review, architecture guidance, and pattern-setting, not just your own output.
• Help less experienced engineers get better at using AI tooling effectively. Not through training decks—through working alongside them.

Prototype and Prove Things Out

• Turn business ideas into working prototypes quickly enough that stakeholders can react to real software, not slides.
• Build proof-of-concepts, internal tools, dashboards, and client-facing portals. Some will ship; some will inform decisions. Both matter.
• Generate architecture options, technical documentation, and solution accelerators using AI—then apply judgment about which option is actually right.

Expertise Required :

What We’re Looking For

• 10–15+ years of professional software engineering experience, with meaningful time spent building and shipping production systems.
• Strong full-stack fundamentals: React/TypeScript, Node.js or .NET, REST APIs, microservices, relational and document databases.
• Working experience with Azure cloud services (or equivalent depth in AWS/GCP with willingness to context-switch).
• Proven, practical use of AI development tools (Claude Code, Copilot, Cursor, or similar) in actual delivery—not just experimentation.
• Experience designing or building agentic AI systems: autonomous agents, multi-agent orchestration, tool-use architectures, or agent evaluation frameworks.
• Familiarity with data engineering concepts and platforms (Databricks, Spark, or similar) sufficient to build data-aware applications.
• Ability to work independently with minimal direction. You should be comfortable with ambiguity and confident making architectural decisions without a committee.

Preferred

• Experience with agent orchestration frameworks (LangGraph, CrewAI, Autogen, Semantic Kernel, or custom implementations).
• Experience with MuleSoft or enterprise integration platforms.
• Hands-on work with LLM APIs, prompt engineering, RAG pipelines, and fine-tuning workflows.
• Familiarity with DevOps practices, CI/CD pipelines, …