Senior Delivery Manager

Robots & Pencils is an applied AI engineering firm building the next frontier of business architecture. We design and ship AI co-workers that integrate into enterprise operations and deliver measurable results for our clients. We're all in on AWS, combining deep UX capability with senior engineering talent to get AI into production fast and keep it there.
We’ve earned the trust of leaders across Consumer Products and Retail, Education, Energy, Financial Services, Healthcare, and Manufacturing and more, and earned a reputation as the nimble alternative to traditional global systems integrators. Founded in 2009, with delivery centers in Canada, the United States, Eastern Europe, and Latin America, we are smaller, faster, and more senior by design. Our teams average 15+ years of experience. We move fast, sweat the details, and build things that actually ship.

Position Overview

As a Senior Delivery Manager, you are the senior delivery authority on a cross-functional engagement: owning execution from kickoff through release, leading distributed onshore-and-offshore teams, and serving as the primary delivery partner to the client. You bring the judgment to navigate ambiguity, the discipline to keep complex programs on track, and the instinct to make every engagement a foundation for the next one.

Why This Role Matters

Senior Delivery Managers are where Robots and Pencils’ promise of “production-ready AI in 30 to 45 days” actually has to hold. On the engagements you run, you set the pace of delivery, the discipline behind it, and the trust that decides whether the client comes back for more. The work moves only as fast as someone is willing to own the plan, surface trade-offs early, and keep distributed teams pulling in the same direction.

What You’ll Do

Delivery

• Lead end-to-end delivery of complex, multi-phase engagements, ensuring alignment across scope, budget, timeline, and quality to meet delivery commitments and achieve successful outcomes
• Facilitate sprint planning, backlog refinement, and delivery execution across one or more cross-functional teams
• Manage phased releases, production readiness, and transitions into operations and maintenance models
• Identify and proactively manage dependencies, risks, scope changes, and escalations before they become blockers
• Define, track, and report delivery KPIs, using data to guide decisions, manage delivery health, and inform tradeoffs across scope, timeline, and budget
• Support QA and UAT efforts, ensuring quality is validated and outcomes meet agreed acceptance criteria
• Bring an AI-forward mindset to your daily work, using tools like Claude to enhance delivery planning, reporting, and stakeholder communications

AI Enablement

• Help implement the AI Development Life Cycle (AIDLC) across your engagements, and set the standard other delivery managers follow for standing it up
• Use AI yourself day-to-day, and set the standard for how your engagements run an AI-augmented delivery process — modeling and coaching the delivery managers you mentor on how planning, building, and review change when agents are doing part of the work
• Know observability for agentic and AI-driven work well enough to partner credibly with an AI product manager and AI engineers across your engagements — judge whether their approach is actually solid, catch risks early, and drive them to resolution — and hold the delivery managers you mentor to the same bar. You’re not the one building the instrumentation, but none of it should be a black box to you.
• Define what “success” looks like for an AI-augmented delivery process and set the metrics standard across your engagements — quality, rework, review overhead, and velocity, not just tests passed
• Build — or direct the building of — status-visibility tooling for complex AI-driven pipelines, using AI yourself, and expect the delivery managers you mentor to do the same
• Understand context management and latency trade-offs deeply enough to make the call on when an agentic approach is the wrong tool for a client’s needs, and defend that call to the client
• Set the standard for what infrastructure an agentic delivery pipeline needs — compute, orchestration, logging, guardrails — and know enough to hold engineering accountable for it, even though you’re not the one building it
• Own the delivery approach for AI-native and agentic engagements: partner with AI/ML engineering leads on model risk, data readiness, and evaluation strategy, and translate technical uncertainty into a delivery plan and risk register the client can act on
• Work with AI product managers and AI engineers to set the standard, across your engagements, for what a “definition of done” looks like for probabilistic or AI-driven features — evaluation thresholds, human-in-the-loop checkpoints, post-launch monitoring — well enough to judge whether it’s actually good, and hold the team to it
• Coach delivery managers and delivery coordinators you mentor on the difference between shipping an AI product, using AI to accelerate a traditional delivery, and running an agentic development process to build the work itself — and help them adapt scope, staffing, and risk management accordingly
• Serve as a credible voice with clients and internal leadership on what “production-ready AI” actually requires — setting realistic expectations for iteration, evaluation, and drift over time
• Serve as the primary delivery contact for clients, building trusted relationships through transparency, consistency, and clear communication while identifying opportunities to expand value within the engagement
• Lead stakeholder working sessions, roadmap alignment discussions, and delivery forums with confidence
• Communicate progress, risks, and tradeoffs clearly to executive and non-technical audiences
• Partner with Client Partners to spot expansion opportunities inside the engagement (scope, follow-on work, new use cases) and help shape the case for what’s next
• Translate delivery progress into business impact for client stakeholders (ROI, milestones, decisions) so executives see ongoing value, not just status

Leadership & Influence

• Lead distributed delivery teams across onshore and offshore locations, fostering accountability, collaboration, and delivery excellence
• Coach junior delivery managers with concrete feedback and stretch opportunities, contributing to their development plans
• Foster a culture of transparency, continuous improvement, and high performance across the engagement
• Contribute to improving delivery practices, tools, and frameworks across engagements
• Assess team strengths and gaps on your engagements, and partner with Client Partners to flag staffing needs and high-potential talent for the firm
• Conduct executive interviews for engineers, designers, and other roles joining your engagements, and serve as an essential voice in go/no-go hiring decisions across the firm

What You’ll Bring

• 5+ years of experience in software delivery, technical project management, or engineering leadership
• Strong understanding of agile delivery practices across complex, multi-team engagements (e.g., Scrum, SAFe, Kanban)
• Experience managing distributed teams across onshore and offshore locations
• Proven ability to deliver complex, multi-platform digital systems in agile environments
• Strong client-facing communication and relationship management skills
• Experience with delivery tooling (e.g., Jira, Confluence, GitHub, Azure DevOps, CI/CD pipelines)
• Familiarity with cloud-native architectures and AI-enabled platforms
• Demonstrable usage of AI tools to enhance delivery planning, reporting, and documentation (e.g., Claude)
• Track record leading at least one engagement where AI/ML or agentic capability was core to the solution — including how you managed model risk, evaluation, and client expectations, not just how you used AI tools to work faster
• Able to set delivery standards for AI-native work across an engagement or practice — evaluation criteria, human-in-the-loop checkpoints, monitoring — and coach other delivery managers on them
• Track record leading a team through an agentic development/delivery process yourself — not just using AI tools personally — including how you approached observability, success metrics, and status visibility for a

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