AI Product Owners

Role Definition

Own the performance reliability and continuous improvement ofAI chatbotssupporting MineStar digital applications.

This role ensures highquality AI behavior by monitoring GenAI performance detecting and reducing hallucinations governing system prompts and improving knowledge inputs. The AI Product Owner works closely with GenAI platform teams SMEs Data owners and Product Support to refine prompts tune chatbot settings and evolve AI capabilities to deliver accurate and trusted responses.

Responsibilities

• AI Bot Performance Ownership:
  Own chatbot performance across accuracy quality efficiency and response reliability; proactivelyidentifyperformance degradation.

• Hallucination Detection & Control:
  Monitor AI responsesidentifyhallucinations and incorrect outputs and define corrective actions to reduce risk and improve trust.

• System Prompt & Configuration Management:
  Create edit tune and govern system prompts and custom chatbot settings including version control guardrails uncertainty handling and escalation logic.

• GenAI Platform Requirements:
  Translate observed gaps and performance issues into clear improvement requirements for GenAI platform teams.

• Knowledge Input Improvement:
  Improve chatbot response quality by refining structuring and updating knowledge articles and source inputs used by the AI.

• Agent Validation & Testing:
  Define and execute validation plans; perform endtoend functional and nonfunctional testing before general availability.

• ApplicationSpecific Ownership:
  Own AI bot behavior for at least one MineStar application (Fleet Health Terrain or Command) ensuring domain correctness.

• Source Data Governance:
  Identify curate add/remove/update/purge source data;maintainmetadata versioning freshness andsingle sourceof truth.

• Support & Incident Governance:
  Triage and resolve AIrelated issues; coordinate fixes with SMEs Data owners and GenAI teams; execute rollback plans when quality degrades.

• CrossTeam Collaboration:
  Resolve dependencies across Product GenAI Data and Support teams to ensure stable chatbot operations.

Immediate Priorities (First 12 Months)

• Establish baselinebot performance and hallucination metrics.

• Stabilize and governsystem prompts and chatbot configurations.

• Improve AI accuracy through betterknowledgearticle inputs.

• Create structured feedback loops with GenAI platform teams.

• Achieve productionready bots with clear rollout and rollback processes.

Success Metrics

• Reduction in hallucinations and incorrect responses

• Improvement in chatbot accuracy relevance and quality scores

• Stability of bot performance across releases

• Faster turnaround on AI defects and enhancements

• Stakeholder confidence in chatbot behavior

Degree Requirement

Required Qualifications:

• Bachelors degree in Engineering Computer Science Data Science or related field.

• 812 yearsof experience in product ownership digital platforms or AIenabled systems.

• Handson experience working withGenAI chatbots or AI agentsin production.

• Proven experience editing governing and tuningsystem prompts and AI settings.

• Strong understanding of AI validation testing and hallucination risks.

• Experience collaborating with GenAIplatform Data Support and SME teams.

• Strong analytical skills and clear communication abilities.

Preferred Qualifications:

• Experience withMineStar applications(Fleet Health Terrain Command)

• Background inProduct Support or Operations

• Experience evaluating AI behavior and response quality

• Familiarity with enterprise knowledge management practices

• Prior work with centralized GenAI platform teams

Top Skills:

• AI chatbot performance management

• Hallucination detection and mitigation

• System prompt design and tuning

• AI validation and quality assurance

• Knowledge content structuring for AI consumption

• Backlog prioritization and requirements definition

Location

• Bangaloreor Chennai

• Onsite role (no remoteoption)

• No travel expected

Key Competencies

• Strong ownership and accountability for AI outcomes

• Bias for quality safety and user trust

• Structured problem solving in ambiguous AI systems

• Continuous improvement mindset

• Clear communicator across technical and nontechnical teams

• High reliability and followthrough