Manager of Product Delivery and AI Enablement
New
C
CGSSoftware Development
Canada / USA (Remote)Full-TimeManager
SalaryCGS evaluates compensation individually for each selected applicant. Final salary placement within our established parameters is based on multiple criteria, including regional market rates, internal equity considerations, cost-of-living geography, and candidate-specific attributes.
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Job Details
- Experience
- 3+ years
- Required Skills
- JiraCI/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 SDLC.
- Practical experience using AI coding agents and AI-assisted development tools for prototyping and accelerating delivery.
- Hands-on experience configuring AI agents, reusable skills, and Model Context Protocol (MCP) or similar tool/API connections.
- Strong understanding of agent behavior, failure modes, prompt injection, and data security in AI workflows.
- Experience defining guardrails and human oversight for AI including approval gates, logging, and exception handling.
- Advanced knowledge of web architecture, REST APIs, authentication, and cloud environments.
- Fluency in CI/CD concepts, source control, release controls, and incident/defect workflows.
- Advanced Jira and Agile delivery expertise, including backlog design and dependency management.
- Ability to read code, logs, and technical documentation to challenge assumptions and collaborate with engineering.
- Principal-level influence to drive decisions and clarity across teams without direct authority.
Responsibilities
- Lead product and delivery across a portfolio of web applications, aligning business outcomes, user needs, and technical constraints.
- Manage Jira backlog quality by translating needs into initiatives, epics, and user stories while leading prioritization and grooming.
- Coordinate release-train planning across Product, Design, Engineering, Data, and Security stakeholders.
- Partner with engineering leaders on CI/CD flow, test strategy, observability, and release criteria.
- Develop prototypes and proofs of concept using AI coding agents to accelerate requirements and validation.
- Configure and manage production AI agents, skills, and reusable workflows with explicit guardrails.
- Define evaluation criteria for AI workflows, measuring reliability, quality, and business impact.
- Establish human-in-the-loop controls to ensure safe and auditable AI-assisted decision-making.
- Analyze logs, architecture diagrams, and API documentation to accelerate diagnosis and product decisions.
- Coach teams on responsible AI adoption and create reusable standards and playbooks.
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