Role: AI Platform Engineer
Location: Halifax, NS
Work Model: Onsite – 4 Days/Week
Role Overview
We are seeking a hands-on AI Platform Engineer to help build and scale an enterprise agentic AI platform that enables development teams to adopt AI-driven Software Development Life Cycle (SDLC) practices.
This role sits within DevOps/Platform Engineering and focuses on building the reusable platform, agents, tools, skills, and automation that development teams can leverage across the software delivery lifecycle. The ideal candidate is a strong software engineer who has hands-on experience with agentic AI, coding assistants, MCP servers, and modern agent frameworks and can turn individual AI-assisted development workflows into production-grade enterprise capabilities.
Key Responsibilities
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Design and develop AI agents across the complete SDLC, including:
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Requirements/specification creation
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Technical specification generation
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Planning and task generation
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Code implementation
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Build and testing
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Deployment and release
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Post-implementation verification (PIV)
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Post-deployment and operational activities
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Build reusable AI agents, skills, and tools using coding harnesses such as Claude Code, custom skills, MCP servers, and managed agent platforms such as Devin.
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Develop native agentic solutions using frameworks such as LangChain and LangGraph or comparable technologies.
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Implement and extend spec-driven development (SDD) workflows, such as: Specify → Clarify → Plan → Tasks → Analyze → Implement, following approaches similar to GitHub Spec Kit.
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Build platform capabilities that enforce AI-driven development lifecycle gates such as Spec Ready and PR Ready through automated, machine-validated controls.
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Develop production-grade platform services, integrations, APIs, tools, and automation using Python and/or TypeScript/JavaScript.
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Continuously evaluate how frontier AI models and coding agents can automate software engineering activities and convert successful experiments into reusable enterprise capabilities.
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Partner closely with DevOps, Cloud, Security, Platform Engineering, and Development teams to safely deploy agents, tools, and skills into production.
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Build and maintain a centralized skill/tool catalog that can be consumed by development teams across the organization.
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Evaluate emerging agentic AI platforms, frameworks, coding harnesses, and architectural patterns and provide build-vs-buy/adopt recommendations.
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Establish appropriate security, governance, observability, reliability, and operational controls for enterprise AI agents.
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Demonstrate new capabilities to development teams and clearly explain how the underlying agents, tools, workflows, and integrations operate.
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Act as a hands-on technical advisor and champion for teams adopting AI-driven SDLC / AI-DLC, helping teams troubleshoot issues and maximize platform adoption.
Must-Have Qualifications
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Strong software engineering background with hands-on experience developing production-grade applications and services using Python and/or TypeScript/JavaScript.
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Demonstrated hands-on experience building agentic AI solutions using LangChain, LangGraph, or comparable agent frameworks.
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Practical experience with Model Context Protocol (MCP), including developing or integrating MCP servers, tools, and custom capabilities.
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Hands-on experience creating custom skills, tools, workflows, or extensions for coding assistants/harnesses, particularly Claude Code or comparable AI coding platforms.
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Strong practical experience using frontier AI coding assistants for real software engineering activities, including multi-step or multi-agent development workflows.
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Experience designing and implementing AI-powered SDLC, developer productivity, software automation, or developer-platform solutions.
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Strong understanding of LLM/agent architecture, tool calling, context management, orchestration, agent workflows, and automation.
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Experience integrating AI agents with APIs, CI/CD pipelines, source-control systems, developer tools, and enterprise platforms.
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Strong understanding of DevOps, software development lifecycle, CI/CD, cloud platforms, and production operations.
Preferred Qualifications
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Experience with GitHub Spec Kit / spec-driven development or similar specification-first development methodologies.
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Experience with Devin, Claude Code, LangChain, LangGraph, MCP, or other modern agentic development platforms.
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Experience building multi-agent systems and agent orchestration workflows.
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Experience with AWS, Azure, or GCP and enterprise cloud-native architectures.
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Experience with Docker, Kubernetes, GitHub Actions, Jenkins, or similar DevOps technologies.
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Knowledge of AI security, responsible AI, access controls, governance, observability, and enterprise AI deployment patterns.
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Experience working in large-scale enterprise or regulated environments, preferably financial services.
Key Technical Skills
Must Have:
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Python and/or TypeScript/JavaScript
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Agentic AI – LangChain/LangGraph or equivalent
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MCP Servers & Custom Tools/Skills
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Claude Code / AI Coding Harnesses
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AI-driven SDLC / Developer Platform Engineering
Nice to Have: Spec Kit / SDD, Devin, Multi-Agent Systems, CI/CD, Cloud, Docker/Kubernetes, Enterprise AI Governance.
