OverviewMontyCloud is building the future of AI-driven cloud management, and we are hiring a Staff Delivery Program Manager to own end-to-end delivery of complex, cross-functional programs, including the AI-powered capabilities that are core to our platform. You will plan and land multi-pod programs across Product, Engineering, SRE, QA, GTM and Customer Success, working with teams and partners across India and the US.This is a hands-on, staff delivery role. We are looking for someone who brings structured program discipline to a fast-moving SaaS environment, uses data to drive decisions, and treats AI both as something we ship and as a way to run programs better.
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.

Skills and experience required

• 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.

Must-haves and differentiators

• 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.

Strong differentiators

• 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.