Manager of Product Delivery and AI Enablement

CGS Immersive

4d • Remote

About The Position

As the Manager of Product Delivery and AI Enablement, you will lead product delivery across a portfolio of web applications while transforming how product and engineering teams use artificial intelligence to work. You will establish practical, reusable AI-assisted ways of working that improve discovery, requirements, prototyping, backlog quality, delivery predictability, and operational decision-making. This is not a model-development role and it is not an application developer position. You will be a hands-on technical product leader who can use AI coding agents to create prototypes and proofs of concept, configure production agents and reusable workflows, read code and technical artifacts, and validate system behavior. Engineering remains accountable for production application code, architecture, integrations, security implementation, and deployment. The ideal candidate combines principal-level product judgment, strong software-delivery fluency, and an AI-first builder mindset. You know where autonomous execution creates leverage, where guardrails are required, and where a human must review, approve, or take over. You influence several cross-functional teams without relying on direct authority and turn ambiguity into clear priorities, executable plans, and measurable outcomes.

Requirements

• 3+ years of experience in technical product management, digital product delivery, or comparable technical product leadership, including ownership across multiple products or teams.
• Demonstrated record of delivering complex web applications or enterprise products through the full software development lifecycle.
• Practical, current experience using AI coding agents and AI-assisted development tools to create prototypes, investigate technical problems, improve requirements, or accelerate delivery.
• Hands-on experience configuring agents, reusable skills and MCP or comparable tool/API connections; able to explain what you built, how it was evaluated, and where you placed controls.
• Strong understanding of agent behavior and failure modes, including context management, tool calling, permissions, prompt injection, data exposure, non-determinism, hallucination, and unsafe or unintended actions.
• Experience defining guardrails and human oversight for production AI workflows, including approval gates, least privilege, logging, testing/evaluation, monitoring, exception handling, and rollback or kill-switch mechanisms.
• Strong working knowledge of web application architecture, REST APIs, data flows, authentication and authorization, cloud environments, and integration patterns.
• Fluency with CI/CD concepts, source control and branching practices, testing stages, environments, release controls, observability, and incident or defect workflows.
• Advanced Jira and Agile delivery experience, including backlog design, refinement, prioritization, dependency management, defect triage, sprint or flow metrics, and release readiness.
• Ability to read code, logs, architecture diagrams, API specifications, and technical documentation well enough to challenge assumptions and collaborate credibly with engineers.
• Principal-level influence: able to create clarity across teams, make trade-offs visible, communicate technical risk to non-technical leaders, and drive decisions without direct authority.
• Strong judgment about when to automate, when to require human review, and when a process should remain fully human-led.
• Excellent written and verbal communication skills, with the ability to work effectively across regions, functions, and time zones.

Nice To Haves

• Experience leading a product portfolio or release train that spans several engineering squads, shared services, or platform dependencies.
• Experience with enterprise AI governance, security reviews, privacy controls, compliance, or responsible-AI operating models.
• Experience building internal product-management or software-delivery automations that achieved measurable gains in cycle time, quality, adoption, or cost.
• Familiarity with agent evaluations, tracing and observability, retrieval-augmented generation, structured outputs, orchestration patterns, or multi-agent systems.
• Experience with Git-based delivery workflows, API testing tools, cloud platforms, containerized applications, or Kubernetes/OpenShift environments.
• A portfolio of prototypes, agent workflows, reusable skills, technical product artifacts, or process transformations that demonstrates an AI-first builder mindset.

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; lead grooming, prioritization, triage, and readiness reviews.
• Coordinate release-train planning and execution across Product, Design, Engineering, Data, Security, Operations, and business stakeholders; surface risks early and drive timely decisions and escalation.
• Partner with engineering leaders on CI/CD flow, environment readiness, test strategy, release criteria, observability, incident learning, and continuous improvement without assuming ownership of application code.
• Use AI coding agents and rapid-development tools to create prototypes and proofs of concept that clarify requirements, validate workflows, and shorten the path from idea to engineering-ready definition.
• Configure and manage production AI agents, skills, Model Context Protocol (MCP) connections, and reusable workflows that improve product-management and delivery processes.
• Design AI-assisted workflows with explicit scope, least-privilege access, approved tools and data, human approval points, failure handling, auditability, and a clear path to pause, override, or roll back automated actions.
• Define evaluation criteria and operating measures for AI workflows, including output quality, reliability, safety, latency, cost, adoption, cycle-time improvement, and business impact.
• Establish human-in-the-loop controls based on risk: require review for consequential decisions and high-impact actions while automating low-risk, repeatable work.
• Read and reason about source code, logs, architecture diagrams, API documentation, data models, and system dependencies to accelerate diagnosis and improve product decisions.
• Use product, operational, and delivery data to validate behavior, investigate issues, test assumptions, prioritize work, and measure outcomes.
• Identify high-leverage opportunities to improve discovery, documentation, analysis, quality assurance, support, release management, and stakeholder communication through AI-assisted processes.
• Create reusable standards, playbooks, templates, and enablement that help teams adopt AI responsibly and consistently; coach product peers and partners in effective use.
• Maintain a current, evidence-based view of agentic tools and practices; run controlled experiments and recommend adoption, change, or retirement based on measurable value and risk.

Benefits

• Health, Dental, Life Insurance and RRSP
• Remote work from home (available options)
• The opportunity to shape how AI is applied across product and software-delivery practices.
• The opportunity for career growth and development, and access to evolving AI tools and learning opportunities.
• A collaborative and inclusive work environment.
• Performance-based incentive structures and performance metrics designed to offer additional earning potential based on business growth.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed