Manager of Product Delivery and AI Enablement
New
C
CGSSoftware Development
Canada / USA (Remote).Full-TimeManager
Salary not disclosed
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Job Details
- Experience
- 3+ years
- Required Skills
- AgileArtificial IntelligenceJiraCI/CDRESTful APIs
Requirements
- 3+ years of experience in technical product management, digital product delivery, or comparable technical product leadership.
- Demonstrated record of delivering complex web applications or enterprise products through the full software development lifecycle.
- Practical experience using AI coding agents and AI-assisted development tools to create prototypes and accelerate delivery.
- Hands-on experience configuring agents, reusable skills, and MCP or comparable tool/API connections.
- Strong understanding of agent behavior and failure modes, including context management, tool calling, prompt injection, and hallucination.
- Experience defining guardrails and human oversight for production AI workflows, including approval gates, logging, and exception handling.
- Working knowledge of web application architecture, REST APIs, data flows, authentication, and cloud environments.
- Fluency with CI/CD concepts, source control, branching practices, testing stages, and release controls.
- Advanced Jira and Agile delivery experience, including backlog design, dependency management, and sprint metrics.
- Ability to read code, logs, architecture diagrams, and API specifications to challenge assumptions and collaborate with engineers.
- Principal-level influence to drive decisions across cross-functional teams without direct authority.
Responsibilities
- Serve as the product-and-delivery lead across a portfolio of web applications, aligning business outcomes, user needs, technical constraints, roadmaps, release plans, and cross-team dependencies.
- Own Jira backlog quality and operating discipline: translate ambiguous needs into initiatives, epics, user stories, acceptance criteria, dependencies, and measurable outcomes.
- Coordinate release-train planning and execution across Product, Design, Engineering, Data, Security, Operations, and business stakeholders.
- Partner with engineering leaders on CI/CD flow, environment readiness, test strategy, release criteria, observability, and incident learning.
- Use AI coding agents and rapid-development tools to create prototypes and proofs of concept to clarify requirements and validate workflows.
- Configure and manage production AI agents, skills, Model Context Protocol (MCP) connections, and reusable workflows.
- Design AI-assisted workflows with explicit scope, human approval points, failure handling, and auditability.
- Define evaluation criteria and operating measures for AI workflows, including output quality, reliability, safety, and cycle-time improvement.
- Read and reason about source code, logs, architecture diagrams, and API documentation to accelerate diagnosis and product decisions.
- Create reusable standards, playbooks, and templates to help teams adopt AI responsibly.
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