AI Product Manager

Indsafri India Private Limited; South Africa; Hybrid; ZAR 900,000 - 1,400,000; Full time

Job summary

Indsafri India Private Limited is seeking an AI Product Manager to identify opportunities, define requirements and own product roadmap across platforms and use cases. You will guide model choices with researchers and engineers, ensuring responsible AI and commercial outcomes throughout the lifecycle.

The role spans strategy, ownership, and governance, connecting business needs with technical delivery, user experience and pricing strategies.

Qualifications

• Experience in product management and SDLC practices.
• Roadmap development, backlog prioritisation and Agile delivery.
• Foundational to intermediate AI/ML knowledge with relevant certifications.
• Experience with data quality, governance and data readiness for analytics.

Responsibilities

• Identify and prioritise opportunities and define business case.
• Develop AI product requirements and user journeys.
• Inform model and technology choices with cross-functional teams.
• Define features and the roadmap for discovery to release.
• Coordinate responsible AI governance and release readiness.

Skills

Power BI

Tableau

Product Management

A/B Testing

Data Science

Business Analysis

Responsible AI

Stakeholder Engagement

Pricing Strategies

Data Governance

Education

BSc in CS/Engineering

Tools

Azure Monitor

Qlik

Job description

Identify, define, deliver and continuously improve AI products that create measurable business and user value. Translate opportunities into product requirements, features and roadmaps. Guide model and technology choices with specialist teams, establish evaluation and monitoring requirements, and shape product positioning and pricing throughout the product lifecycle.

The role spans AI product strategy and product ownership across platforms and use cases. It provides a clear connection between business needs, user experience, technical delivery, responsible AI and commercial outcomes.

Activities and responsibilities

1 Identify and prioritise opportunities

Investigate user needs, business processes and market opportunities. Define the problem, target users, current performance baseline and intended benefits. Assess whether AI is appropriate and prioritise use cases against value, feasibility, data readiness, cost and risk. Develop the business case and investment priorities with stakeholders.

2 Develop AI product requirements

Own the product requirements document for each use case. Specify intended behaviour, inputs and outputs, scope, user journeys, data requirements, integrations, limitations and measurable acceptance criteria. Describe human review, escalation and fallback needs. Translate business expectations into requirements that delivery teams can implement and evaluate.

3 Inform model and technology choices

Maintain a practical understanding of the AI landscape and emerging technologies. Work with researchers, ML engineers and architects to assess models and solution approaches against product requirements. Frame trade-offs involving quality, response time, cost, explainability, security and deployment constraints. Request relevant benchmarks and experiments to support decisions and revisit choices as evidence changes.

4 Define features and the product roadmap

Translate use cases into clearly articulated capabilities, features and a prioritised backlog. Define what each feature should do and how users will benefit. Build a roadmap covering discovery, experimentation, pilot delivery, release and improvement. Account for data preparation, technical integration and governance dependencies, and adjust scope and sequencing as evidence emerges.

Delivery and lifecycle responsibilities

5 Specify data requirements and readiness

Define required data sources, formats, coverage, volumes, quality checks and labelling needs with specialist teams. Coordinate access and permitted use with data owners. Identify gaps and preparation dependencies, and incorporate them into delivery estimates and the roadmap. Document relevant data handling constraints for review by the appropriate control teams.

6 Establish product and solution evaluations

Own the evaluation plan with data scientists, ML engineers and domain experts. Define metrics, baselines, targets, test populations, measurement methods and evaluation periods. Include representative, difficult and failure cases. Evaluate individual capabilities and the complete user workflow, including human intervention and downstream effects. Agree acceptance thresholds, release criteria and the evidence needed for each decision.

7 Monitor performance and improve the product

Specify telemetry, user feedback and reporting requirements. Review quality, adoption, outcomes, failures and cost over time. Evaluate changes to models, prompts, data and workflows before release. Prioritise improvements based on evidence and coordinate responses when performance falls below agreed thresholds, including escalation or rollback where appropriate.

8 Coordinate responsible AI and release readiness

Engage governance, risk, security, legal and compliance stakeholders early. Explain the product purpose, functionality, inputs and outputs, data handling, limitations and proposed controls. Incorporate their requirements into the backlog and release process. Coordinate evidence for review and ensure product claims reflect demonstrated capability. Maintain visibility of approvals, operational ownership and unresolved risks.

9 Define positioning and pricing

Develop the value proposition and product positioning. Understand the full cost of data, models, infrastructure, integration, operations and human review. Compare relevant subscription, consumption and outcome-based pricing approaches where applicable. Assess cost per successful outcome and sensitivity to usage. For internal products, work with Finance and business owners on transparent consumption measures, cost allocation and transfer-pricing proposals.

10 Drive adoption and value realisation

Align business and technical stakeholders around priorities, delivery expectations and product outcomes. Support training, change management and user adoption. Track realised benefits against the business case and review whether the product should expand, change or retire. Manage budget and supplier dependencies with the relevant owners and communicate decisions and progress clearly.

Accountability and decision boundaries

The AI Product Manager owns product outcomes, requirements, priorities and the evaluation framework. Researchers, engineers and architects own technical implementation and specialist design decisions within their mandates. Governance and control functions retain their approval responsibilities. Finance and business owners agree pricing and internal charging arrangements with the product manager.

Expectations from delivery partners

Data engineers: Clear data specifications, quality criteria, access and usage constraints, and priorities for closing readiness gaps.

Data scientists: Defined objectives, metric definitions, evaluation questions, representative cases, telemetry and feedback needs.

Researchers and ML engineers: Product constraints, testable hypotheses, benchmark priorities, access to evaluation resources and review milestones.

Engineering MLOps and design: User journeys, feature behaviour, integration requirements, operational expectations and release acceptance criteria.

Governance and control teams: Plain-language product scope, data handling, limitations, proposed controls and evidence for review.

Business and Finance: Value case, prioritised roadmap, budget and cost visibility, adoption measures and pricing or cost-recovery proposals.

Qualifications and experience

• Experience in product management, product discovery, business analysis, or software development lifecycle (SDLC) practices, including requirements elicitation, user story development, and solution design
• Demonstrated experience in product roadmap development, backlog prioritisation, Agile delivery methodologies, and cross-functional product delivery
• Foundational to intermediate knowledge of Artificial Intelligence (AI), Machine Learning (ML), and Generative AI, supported by relevant certifications (e.g., Microsoft AI certifications, Claude Associate, OpenAI, AWS, Google, or equivalent AI credentials)
• Experience working with data-driven products, including data quality assessment, data governance, data modelling, and ensuring data readiness for analytics and AI use cases
• Experience designing, managing, or supporting pilots, proof-of-concepts (PoCs), A/B testing, and model evaluation frameworks
• Proficiency in analytics and performance measurement tools (e.g., Power BI, Tableau, Qlik, Azure Monitor, Application Insights, or equivalent) to track business, product, and operational outcomes
• Knowledge of Responsible AI principles, AI governance frameworks, risk management, regulatory compliance, privacy, and ethical AI practices.
• Experience developing business cases, financial models, ROI assessments, cost-benefit analyses, and pricing strategies for digital, data, or AI-enabled products and services
• Strong stakeholder engagement experience, with a proven ability to communicate complex technical concepts to business audiences, facilitate decision-making, and support change management and adoption initiatives

Preferred Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, Business Science, or a related field.
• Professional certifications in Product Management (PSPO, CSPO, SAFE POPM), Agile Delivery, Business Analysis (CBAP, PMI-PBA), or AI and Data disciplines.
• Experience delivering AI, analytics, digital transformation, or enterprise technology initiatives within large, complex organisations.

Skills

Power BI Tableau Product Management A/B Testing Data Science Business Analysis Risk management Generative AI Regulatory Compliance Data Modelling Requirements Elicitation Data Governance solution design information systems change management Backlog Prioritisation Application Insights Azure Monitor Qlik Computer Science Responsible AI principles Engineering Artificial Intelligence (AI) Machine Learning (ML) Product Discovery Privacy Data Quality Assessment Cost-Benefit Analyses User Story Development Agile Delivery Methodologies Cross-functional product delivery Product roadmap development Software Development Lifecycle (SDLC) practices Data readiness for analytics and AI use cases Supporting pilots Data-driven products Managing pilots Proof-of-concepts (PoCs) Analytics and performance measurement tools AI governance frameworks Designing pilots Model evaluation frameworks Ethical AI practices Pricing strategies for digital products Financial models ROI assessments Developing business cases Pricing strategies for data-enabled products Strong stakeholder engagement Communication of complex technical concepts to business audiences Delivering analytics initiatives Pricing strategies for AI-enabled products and services Adoption initiatives Facilitate decision-making Delivering digital transformation initiatives Delivering AI initiatives Delivering enterprise technology initiatives Business Science