Company: Codvo.ai
Business / Product Line: NeIO Industrial and Enterprise AI Solutions
Experience: 10–12+ years
Employment Type: Full-time
Role Level: Senior Product Owner / Product Lead
Reporting To: Head, Product and Solutions
Location: Remote/ Pune
Domain Priority: Datacenters and other critical infrastructure

About Codvo.ai

Codvo.ai builds AI-native products and solutions that help enterprises apply governed intelligence to high-value operational and business workflows. Our NeIO portfolio combines domain intelligence, machine learning, agentic AI and enterprise-grade deployment patterns for organisations operating in complex, regulated and mission-critical environments.

We are expanding our industrial predictive-intelligence and agentic-AI portfolio for Datacenters and other critical-infrastructure industries. Our objective is to help customers move beyond dashboards and reactive alarms by identifying developing operational risks, explaining the supporting evidence and orchestrating appropriate responses—with human governance and within the customer’s approved technology environment.

Why This Role Exists

Codvo.ai is maturing a differentiated set of industrial predictive-intelligence and agentic-AI solutions into scalable, launch-ready products. We need a senior techno-functional Product Owner who can translate Datacenter, plant-floor, reliability, maintenance and process-operations problems into repeatable products rather than one-customer customisations.

This role requires more than backlog administration. The Product Owner must understand what false alarms, missed detections, weak explanations and unusable warning horizons mean operationally. They will own the product vision, roadmap, backlog, release decisions, customer validation, launch readiness and commercial outcomes required to move products from discovery and pilot to production adoption and multi-site scale.

The ideal candidate will have built or managed B2B industrial, Datacenter, energy, asset-performance, operational-technology or critical-infrastructure products. Experience with organisations such as Schneider Electric, Honeywell, AVEVA, Rockwell Automation, Siemens, GE Vernova, Baker Hughes, ABB, Emerson, Johnson Controls—or comparable industrial technology and enterprise product companies—would be highly relevant.

What the Role Owns

The customer problem, product vision, target market, personas, value proposition and differentiated position.
The product roadmap, prioritised backlog, MVP, Beta and GA boundaries, acceptance criteria and release readiness.
The journey from customer discovery through pilot, launch, adoption, production conversion and commercial growth.
The boundary between standard product capability, configuration, partner extension and custom solution engineering.
Product performance, customer value, operator adoption and commercial outcomes.

Key Responsibilities

1. Product Strategy, Market Definition and Discovery

Own the product vision, strategy, ICP, user and buyer definitions, priority use cases, value proposition and multi-horizon roadmap for industrial predictive-intelligence and agentic-AI solutions.
Lead structured discovery with Datacenter operators, reliability and maintenance teams, process engineers, digital leaders, OT teams, security stakeholders and commercial buyers.
Map operating workflows, failure modes, decisions, pain points, data readiness, integration constraints and measurable outcomes.
Translate FMEA and RCM thinking, condition-monitoring practices, operating regimes and practitioner knowledge into reusable product capabilities.
Use customer evidence, market analysis and competitive intelligence to guide product investment and sequencing decisions.
Balance reusable platform capabilities with industry-specific products, asset models and domain intelligence.

2. Roadmap, Backlog and Cross-Functional Delivery

Own and prioritise the roadmap and backlog; define user stories, acceptance criteria, release scope, product trade-offs and evidence-based exit criteria.
Define clear MVP, Beta and GA boundaries and ensure that each release addresses a validated customer or operational outcome.
Run a disciplined product cadence with Engineering, Data Science, ML Engineering, UX, QA, Solution Architecture and industrial domain specialists.
Accept or reject delivered capabilities against agreed product outcomes, quality standards and operational use cases.
Balance customer value, operational safety, technical feasibility, product reuse, time to market and commercial opportunity.
Maintain a transparent roadmap and decision record for executives, delivery teams and GTM stakeholders.

3. Predictive Intelligence, AI and Data Lifecycle

Define the end-to-end product lifecycle for equipment onboarding, tag mapping, signal-quality validation, feature engineering, model calibration, inference, prediction publishing and feedback capture.
Establish product standards for fault libraries, feature definitions, physics constraints, synthetic datasets and asset-family model coverage.
Shape coherent experiences across physics-informed models, synthetic fault trajectories, anomaly detection, fault classification, risk scoring and remaining-useful-life estimation.
Define evaluation requirements covering detection quality, false-alarm and missed-event costs, warning lead time, confidence, explainability, human review, drift and evidence traceability.
Ensure that prediction outputs are useful to operators—not merely technically accurate—including probable fault diagnosis, supporting evidence, contributing-feature analysis and recommended next steps.
Help establish governed processes for model monitoring, operator feedback, model updates and fault-library expansion.
Ensure that product and GTM claims accurately communicate capabilities, limitations, data dependencies and validation requirements.

4. Agentic Product Experience and Governance

Shape governed agentic workflows that can observe, diagnose, explain and orchestrate—with appropriate human approval.
Define agent roles, decision boundaries, escalation paths, approval gates, audit trails, fallback behaviour and role-based controls.
Translate model evidence into understandable diagnoses, contributing factors, operational context and recommended actions.
Ensure that agents augment operators, engineers and maintenance teams rather than creating opaque or ungoverned automation.
Define how agentic workflows interact with maintenance, reliability, operator-response and work-order processes.

5. Industrial Integration, Deployment and Production Readiness

Define requirements for sensor and telemetry data, asset hierarchy, event and work-order history, operating context and enterprise integrations.
Support integration with relevant systems such as BMS, SCADA, DCS, PLC, DCIM, EPMS, MES, industrial historians, IoT platforms and CMMS/EAM solutions.
Work with architecture and security teams to support appropriate cloud, edge, on-premises, containerised and air-gapped deployment patterns.
Incorporate data sovereignty, access control, auditability, resilience, cybersecurity, upgrade management and model governance into the roadmap.
Define how customers consume intelligence through the NeIO workbench, approved APIs, existing user interfaces and connector frameworks.
Ensure that deployment, configuration and integration methods are documented, supportable and commercially repeatable.

6. Pilot Design, Validation and Production Conversion

Define repeatable discovery, assessment, proof-of-value and accelerated-pilot methods.
Establish entry criteria covering asset selection, telemetry availability, data quality, fault coverage, customer participation, deployment readiness and known constraints.
Agree success measures and go/no-go criteria before execution, including prediction usefulness, warning lead time, explainability, operator acceptance and workflow readiness.
Define the boundary between product configuration, reusable roadmap capability and customer-specific engineering.
Ensure that pilots generate reusable product learning and proof rather than ending as isolated demonstrations.
Convert pilot findings into roadmap actions, implementation improvements, commercial evidence and a plan for production and multi-site expansion.

7. Productisation, Launch, GTM and Growth

Drive products through discovery, validation, launch and scale using evidence-based readiness criteria.
Co-own positioning, messaging, packaging principles, demonstrations, pricing inputs, proof points and sales enablement with GTM teams.
Develop the product inputs required for solution decks, demonstrations, FAQs, battlecards, discovery guides and objection handling.
Participate in strategic customer conversations, industry events, analyst discussions and partner engagements.
Define adoption, usage, time-to-value, retention, expansion, pilot-to-paid conversion and commercial-performance indicators.
Identify growth opportunities across sites, equipment families, workflows and adjacent critical-infrastructure industries.
Help shape OEM, platform, system-integrator and channel-partner strategies that accelerate market access and adoption.

8. Cross-Functional Leadership

Act as the single accountable product voice across business, technology and customer-facing teams.
Build alignment without relying solely on formal authority and resolve ambiguity across competing requirements.
Communicate product strategy, trade-offs, delivery progress and evidence clearly to executives, technical teams and customer stakeholders.
Mentor product managers or business analysts as the portfolio and product organisation grow.

Required Experience and Qualifications

10–12+ years of professional experience, including substantial end-to-end ownership of B2B industrial, Datacenter, energy, asset-performance, operational-technology or critical-infrastructure products.
Evidence of taking a complex enterprise product through discovery, multiple releases, production validation, launch and customer adoption; project coordination alone is insufficient.
Functional credibility in asset-intensive operations and concepts such as failure modes, condition monitoring, predictive maintenance, alarm quality, maintenance strategy and operational risk.
Direct customer-discovery experience with practitioners and buyers, converting operating workflows and imperfect data realities into prioritised product requirements and acceptance criteria.
Practical AI/ML product judgement covering data suitability, model scope, evaluation metrics, false positives and negatives, explainability, human oversight, monitoring and drift.
Working fluency with industrial data and integration landscapes, including telemetry, BMS, SCADA, DCS, PLC, DCIM, EPMS, MES, historians, IoT and CMMS/EAM systems.
Strong roadmap, backlog, release-management and prioritisation discipline, with the confidence to make and defend difficult product trade-offs.
Meaningful responsibility for product GTM, positioning, launch, packaging, enterprise sales enablement, adoption or growth.
Experience working directly with enterprise customers, senior stakeholders and multidisciplinary implementation teams.
Executive-ready written and verbal communication and the ability to align Engineering, Data Science, SMEs, GTM teams, partners and customers.
Bachelor’s degree in Engineering, Computer Science, Information Systems, Industrial Engineering or another relevant technical discipline.
MBA or comparable postgraduate qualification in business, strategy, product management or technology management.

Preferred Background

Product experience in Datacenter infrastructure, cooling, electrical systems, energy management, asset-performance management, predictive maintenance, industrial automation or process industries.
Experience with a recognised industrial technology, engineering-software or enterprise product organisation such as Schneider Electric, Honeywell, AVEVA, Rockwell Automation, Siemens, GE Vernova, Baker Hughes, ABB, Emerson or Johnson Controls, or a comparable company.
Exposure to chillers, CRAH/CDU systems, pumps, UPS, transformers, generators, batteries, gas turbine, rotating equipment, process equipment or other critical assets.
Working knowledge of physics-informed AI, synthetic data, anomaly detection, predictive models, digital twins or remaining-useful-life methods.
Familiarity with SAP PM, IBM Maximo, industrial historians, major automation stacks or Datacenter infrastructure-management platforms.
Experience with edge computing, container platforms such as Red Hat OpenShift, on-premises or air-gapped environments.
Experience shaping enterprise product positioning, pricing and packaging, pilots, value-realisation frameworks and land-and-expand adoption.
Experience building products through OEM, system-integrator, channel-partner or technology-partner ecosystems.

What Success Looks Like

First 90 Days

Build a clear understanding of the portfolio, target customers, current capabilities and delivery constraints.
Validate priority customer problems, buyers, Datacenter use cases and product differentiation.
Establish a prioritised roadmap and launch-readiness plan.
Define product boundaries, standard capability expectations and pilot-success criteria.
Create a dependable operating cadence across Product, Engineering, AI/ML, Solutioning and GTM teams.
Improve product completeness across equipment onboarding, model outputs, explanations and agentic workflows.
Establish consistent customer-discovery, pilot and production-conversion methods.
Strengthen product documentation, implementation readiness and GTM enablement.
Generate validated customer evidence and incorporate the findings into the roadmap.

Product Success Measures

Quality, clarity and validation of the product roadmap and investment decisions.
Time from customer discovery to a testable product outcome.
Pilot readiness, completion, customer acceptance and conversion to production.
Operator adoption, product usage and time to value across sites, assets and workflows.
Prediction usefulness, warning lead time and explainability in agreed customer contexts.
Time required to onboard new sites, telemetry and supported equipment.
Reuse of models, fault libraries, integrations and implementation methods.
Sales, customer-success and partner readiness.
Revenue contribution, account expansion and product-market learning.

Why Join Codvo.ai

Shape a differentiated industrial and enterprise AI portfolio at an important stage of product maturity.
Work across predictive intelligence, agentic AI, industrial operations and enterprise platforms.
Influence product strategy, technology direction, customer outcomes, partnerships and GTM execution.
Build products for environments where reliability, operational resilience and governed intelligence matter.
Collaborate with multidisciplinary teams spanning domain expertise, AI/ML, Engineering, Design, Sales and Partnerships.

Senior Product Owner - Industrial Predictive Intelligence and Agentic AI