Project Manager, Product Deployment

Verdantis I Mumbai I On-Site

About the Company

Verdantis builds MRO360, an AI-native platform that helps maintenance, repair, and operations (MRO) teams in asset-heavy industries clean, enrich, reason over, and act on their spare parts, inventory, procurement, and reliability data.

About the Role

You will own the delivery of Verdantis's AI-native products- MRO360 and our MDM at large asset-intensive enterprises in energy, manufacturing, mining and CPG. Most customers run SAP (ECC or S/4HANA) and have spent two decades buying record systems for maintenance that still leave the thinking to people: which parts to stock, which materials are duplicates, which equipment is critical. We believe AI agents can now do that thinking continuously, across every plant, at a cost no consulting engagement can match and that this change who wins the maintenance software market. Your job is to make that real for each customer, from signed contracts to a live system that is making decisions the plant trusts.We are AI-native in how we build and how we deliver. The product is designed around agents rather than screens, and the delivery team uses the same agents to profile data, draft documents and generate tests. Every deployment is both a customer outcome and evidence that the model works.This is a product delivery role, not a governance role. You know how a product org works, what a PM owns versus what a tech lead owns, how a sprint is planned, what a release train looks like, and you use that to get customer needs built and shipped without breaking the roadmap. You will not be forwarding status emails between them.

Responsibilities

• What you doRun 3-5 concurrent customer deployments end to end: scoping, data onboarding, configuration, integration, UAT, go-live and hypercare.
• Own the delivery plan, RAID log, weekly customer steering updates and the single source of truth on scope. Hold the line on scope creep and call out slippage early, with options.
• Work the product org from the inside: write customer requirements as product-ready stories, sit in backlog grooming with the product manager, agree technical approach and effort with the tech lead, and track the commitment through sprints to release.
• Separate product gaps from configuration and change-management problems before anything reaches engineering; say no to the customer when the ask is a workaround.
• Lead data onboarding with the BA team: SAP material master, equipment and BOM extracts (MARA/MARC/MBEW/EQUI and friends), cleansing rules, deduplication review and load sign-off.
• Design the integration approach with customer IT: flat-file, IDoc, OData or middleware and get it approved by their SAP basis and security teams.
• Drive adoption of the AI recommendations: identify the planner, buyer and reliability personas, run enablement, and track whether the plant is acting on the agent's output after go-live, not just whether go-live happened.
• Use Verdantis's own AI agents in your work for document generation, data profiling, test-case creation and codify what works in the delivery playbook so each deployment is faster than the last.
• Protect margin: track hours against the SOW, flag change requests, and hand a clean, referenceable customer to Customer Success.

Qualifications

• Must-have experienceProduct delivery experience6-10 years in enterprise software, of which at least 4 deliver a SaaS or packaged product into customer environments not internal IT projects, not pure consulting decks.
• Has personally taken at least 3 enterprise deployments from kick-off to go-live and can describe what went wrong on each and what they changed.
• Having worked inside a product organization knows how to turn a customer ask into a story a product manager will accept, how to negotiate scope and dates with a tech lead, and how to read a sprint board and know whether a release is at risk.
• Comfortable opening the data: can read a customer extract, spot a broken key or a bad unit of measure, and write the cleansing rule, not just assign the ticket.

Required Skills

• SAP fundamentalsWorking knowledge of SAP MM and PM: material master, plant/storage location structure, reservations, reorder points, equipment and functional locations, BOMs.
• Has been on either side of an SAP integration (extract, IDoc/BAPI/OData, or middleware) and knows what to ask a customer's basis team.
• Does not need to be a certified SAP consultant; does need to hold their own in a room with one.
• Appreciates what AI changesHas used LLM-based tools in their own delivery work and can give specific examples of where it saved days and where it produced garbage.
• Can explain to a skeptical maintenance manager why an AI recommendation is trustworthy, and what the human-in-the-loop controls are.
• Sees AI to compress the deployment timeline, not as a feature to be demoed.
• Working styleWrites clearly: a one-page status note a CFO can read, and a technical note an integration engineer can act on.
• Runs tough conversations directly- a slipping customer resource, a scope change that costs money, an engineer who missed a date.
• Willing to travel to customer sites, including internationally, 20-30% of the time.

Preferred Skills

• Nice to haveDomain exposure to MRO, spare parts, plant maintenance or procurement in oil & gas, chemicals, mining, utilities, paper & packaging or CPG.
• Experience with Maximo, Oracle EAM or other EAM/ERP systems in addition to SAP.
• Prior work at an early-stage or scaling B2B SaaS company where the playbook was still being written.
• Basic SQL or Python for data profiling.
• Who this role is not forSomeone whose main output is the Gantt chart and the status deck. We need the person who makes the Gantt chart come true.
• A PMO or governance specialist who has not personally sat with a customer's data or integration team.
• Someone who wants a fixed scope handed over and a team to delegate to. Scope here is negotiated live and the team is small.
• Someone who treats AI as a buzzword to put on slides, or as a threat to be kept out of their process.