About the role
Role summaryYou will be the strategic delivery partner to Product, Engineering, SRE, GTM, Customer Success and Portfolio leadership. You will own delivery across two to four cross-functional agile teams (PODs), from quarterly/monthly planning through release, production stability and feature adoption.
The role spans three kinds of programs:
- Product and Platform delivery: roadmap features, multi-cloud capabilities (AWS, Azure, GCP), integrations and releases.
- AI-Driven Programs: AI and agentic features in the MontyCloud platform, from model evaluation and data readiness through launch, adoption and cost ROI.
- Engineering Health Programs: Tech debt management, release quality and production-issue reduction, made visible and prioritized with data alongside feature work.
Key responsibilitiesDelivery ownership and execution
- Own monthly/quarterly roadmap planning with Product; bring data on customer demand, CS business impact, Tech debt capacity. Handle trade-off between multiple prioritized features backed with data, not assumption.
- Own end-to-end delivery of product and engineering workstreams: milestones, risks, metrics and executive reporting.
- Drive Sprint intake from idea to backlog (Epics/Features) and enforce backlog hygiene: stories, acceptance criteria and estimates ready before the sprint starts.
- Manage cross-pod dependencies proactively, with dependency tickets raised on the receiving pod's board well before planning.
- Maintain a RAID log from the program charter onward, and partner with the Review Board so technical risks and mitigations are captured early.
- Handle urgent business asks (ELT, Large customers, GTM) by negotiating scope and sequencing between Product and Engineering without derailing committed work.
AI-driven programs
- Deliver AI and agentic capabilities in the MontyCloud platform end to end, from problem framing and model or approach evaluation through pilot, production and adoption.
- Run structured evaluation gates: pilot criteria, quality and accuracy benchmarks, and Go/No-Go decisions on which models or approaches move forward.
- Drive data readiness for AI features, including evaluation datasets, annotation and dogfooding programs across Engineering, CS and other functions.
- Embed responsible-AI checkpoints into delivery: data privacy and PII handling, security review, customer data boundaries and human-in-the-loop design where needed.
- Track AI effort and spend (engineering capacity, model and token costs) against outcomes, and give leadership a clear ROI view, including when to stop, pivot or scale.
- Define and track AI feature adoption and value metrics with Product and Customer Success after launch.
Release governance and quality
- Lead end-to-end release management: release calendar, Go/No-Go reviews and cross-product/cross-portfolio coordination.
- Own a live release health view covering burndown per pod, E2E pipeline runs, QA defects, open dependencies and security vulnerabilities on release candidates.
- Drive down production issues by categorizing them through RCA (test gap, infra, product gap) and turning each category into a preventive action owned by QA, SRE or Product.
- Ensure SRE and Customer Success are enabled on every release, so new features are supportable and actually used.
- Improve release cadence and predictability over time; we value candidates who have shortened release cycles without losing quality.
Agile and program discipline
- Implement and run agile practices across PODs: sprint planning, grooming, reviews, retrospectives, and Inspect & Adapt workshops.
- Set in-sprint checkpoints (burndown milestones, early QA involvement, next-sprint readiness) so issues surface mid-sprint, not at the end.
- Run retrospectives at two levels, per POD and across the program, and track improvements through to closure.
- Establish measurable KPIs for each program tied to product KPIs: say/do ratio, predictability, cycle time, defects, production issues and feature adoption.
- When a POD consistently misses commitments, diagnose the cause with data (capacity, planning quality, dependencies, scope pressure) and drive the fix with the EM and PM.
Stakeholder and team leadership
- Act as strategic advisor to Product and Engineering leadership; make decisions clear and fast, and hold cross-functional teams accountable.
- Build strong partnerships across Engineering, Product, SRE, GTM, CS, QA, UX and Operations.
- Make the case for non-feature work (tech debt, platform standardization) with data, and win buy-in from Product and senior leadership.
- Lead capacity and estimation discussions, ensuring estimates cover development, testing, test automation and monitoring.
- Present program status, risks and outcomes to leadership through concise dashboards and scorecards; mentor technical leads and support onboarding.
- 10+ years in technology delivery, with 6+ years leading high-complexity programs in B2B SaaS product companies (product, engineering, SRE or technical lead background preferred).
- Proven delivery across multiple concurrent agile teams with globally distributed stakeholders.
- Working technical depth to engage engineers credibly: cloud platforms (AWS, Azure, GCP)
- AI-native program management: using tools such as Claude to build dashboards, status reports, risk summaries and planning analysis.
- Strong command of Scrum, Kanban and scaled frameworks (SAFe); agile transformation experience is a mandate.
- Hands-on with Jira, Confluence, including custom fields, dashboards and release reporting.
- Excellent executive communication: turning program data into clear decisions for ELT.
- Bachelor's degree in engineering or a related field.
- Track record of full delivery ownership in a B2B SaaS company, with measurable outcomes (release cadence, predictability, defect or incident reduction).
- Demonstrated partnership across Product, Engineering, SRE, GTM, Customer Success and QA to own end-to-end feature delivery, including post-release adoption.
- Certification: PSM /CSM /SAFe or PMI-ACP.
- Agile/Scrum program management proven at scale across multiple PODs.
- Data-driven governance: has built and used delivery dashboards and KPIs to drive decisions, not just report status.
- Delivered AI-driven programs from evaluation through production, with clear governance on privacy, quality and spend.
- Shortened release cycles or materially improved predictability, with numbers to show it.
- Led a cross-pod engineering health program (tech debt, platform standardization) and won Product buy-in with data.
- MSP/partner customer base or cloud management domain experience.
- SaaS delivery from concept to production.