AI Product Manager – Feature Strategy and Evaluation

The Elite Job; Bethel (CT); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Flexible work options

Professional development

Learning resources

Job summary

The Elite Job is seeking an AI Product Manager – Feature Strategy and Evaluation to lead planning, prioritization, and evaluation of AI-powered product features. You will partner with engineering, data science, UX, and business teams to translate complex AI capabilities into tangible customer value.

You will define product vision, roadmaps, measurable success criteria, and framework-based evaluations throughout the feature lifecycle, while upholding responsible AI practices and data governance.

Qualifications

• Bachelor's degree in a technical field or related discipline.
• Masters in product management, data science, AI, or business is advantageous.
• Certifications in PM, Agile, AI fundamentals, or data analytics are desirable.

Responsibilities

• Develop AI feature strategy, roadmaps, and evaluation criteria.
• Collaborate with engineering, data science, UX, and business teams to prioritize initiatives.
• Design A/B tests, experiments, and performance dashboards for AI features.
• Define KPIs, success metrics, and acceptance criteria for feature releases.
• Monitor AI feature value, reliability, fairness, and privacy throughout lifecycle.
• Communicate results and recommendations to stakeholders clearly.

Skills

Product management

AI concepts

KPIs & metrics

Stakeholder management

Agile methodologies

Education

Bachelor's degree in CS/AI/DS

Masters in PM/AI (advantage)

PM/AI certifications (desirable)

Tools

Jira

SQL

Experimentation tools

Job description

1. Job Summary

The Elite Job is seeking a strategic, analytical, and customer-focused AI Product Manager – Feature Strategy and Evaluation to lead the planning, development, prioritization, and evaluation of AI-powered product features. This role is ideal for a professional who understands the intersection of artificial intelligence, product strategy, user experience, and business performance and can translate complex technical capabilities into practical, valuable solutions.

The successful candidate will work closely with engineering teams, data scientists, machine learning engineers, UX/UI designers, business stakeholders, and customer-facing teams to identify opportunities for AI-driven innovation. The primary objective is to develop a clear product vision, establish feature roadmaps, define measurable success criteria, and evaluate AI features throughout their lifecycle.

The AI Product Manager will assess customer needs, market trends, competitive offerings, and emerging AI technologies to determine which features should be developed, improved, tested, or discontinued. Responsibilities will include gathering product requirements, prioritizing development initiatives, designing evaluation frameworks, coordinating experimentation, and monitoring feature performance after release.

A critical component of this position is ensuring that AI-powered features deliver measurable business value while maintaining reliability, usability, fairness, transparency, privacy, and responsible AI practices. The ideal candidate combines strong product management fundamentals with a working understanding of machine learning, generative AI, model evaluation, experimentation, and data-driven decision-making.

This position offers an opportunity to shape AI product experiences, influence strategic decisions, and contribute to the development of intelligent products that solve real customer problems.

2. Key Responsibilities

AI Product Strategy and Vision

• Develop and communicate a clear product vision, strategy, and roadmap for AI-powered features aligned with organizational objectives and customer requirements.
• Identify opportunities to incorporate machine learning, generative AI, automation, recommendation systems, and intelligent decision-support capabilities into existing or new products.
• Translate business goals and customer challenges into actionable product initiatives, feature specifications, and measurable outcomes.
• Evaluate emerging AI technologies and industry developments to identify opportunities for differentiation and product innovation.
• Collaborate with leadership to establish product priorities, define investment opportunities, and communicate strategic recommendations.

Feature Planning and Prioritization

• Own the AI feature lifecycle, from initial discovery and opportunity assessment through development, launch, evaluation, and continuous improvement.
• Maintain a prioritized product backlog based on customer impact, business value, technical feasibility, development effort, operational risk, and strategic alignment.
• Develop product requirement documents, user stories, acceptance criteria, workflow specifications, and feature definitions.
• Facilitate prioritization discussions with engineering, data science, design, and business teams to balance competing requirements.
• Establish clear decision-making criteria for introducing new features, improving existing functionality, or retiring underperforming capabilities.
• Identify dependencies, resource requirements, delivery risks, and implementation constraints early in the product development process.

AI Feature Evaluation and Performance Measurement

• Design evaluation frameworks to assess the accuracy, relevance, usefulness, reliability, and consistency of AI-powered features.
• Define key performance indicators (KPIs), product metrics, evaluation benchmarks, and acceptance thresholds before feature releases.
• Collaborate with data scientists and machine learning engineers to evaluate model outputs, compare alternative approaches, and assess feature effectiveness.
• Establish processes for collecting user feedback, analyzing usage patterns, identifying failure cases, and prioritizing improvements.
• Coordinate A/B testing, controlled experiments, usability studies, and pilot launches to validate product hypotheses.
• Monitor feature performance after deployment and recommend adjustments based on quantitative evidence and qualitative feedback.
• Assess the impact of model changes, prompt modifications, data updates, and new product functionality on the overall user experience.
• Ensure that evaluation results are documented and communicated clearly to technical and non-technical stakeholders.

Customer Research and Market Analysis

• Conduct customer interviews, surveys, usability sessions, and stakeholder discussions to understand user expectations and unmet needs.
• Analyze customer behavior, product usage data, support feedback, and market research to identify high-value feature opportunities.
• Develop customer personas, use cases, user journeys, and problem statements to guide feature development.
• Research competing AI products and industry benchmarks to identify strengths, weaknesses, market gaps, and potential differentiators.
• Validate assumptions through prototypes, minimum viable products (MVPs), and early-access programs before committing significant development resources.
• Translate research findings into actionable recommendations that improve product adoption, satisfaction, and retention.

Cross-Functional Collaboration

• Partner with software engineers, machine learning engineers, data scientists, designers, QA specialists, and business teams throughout the product development lifecycle.
• Communicate product requirements and evaluation criteria clearly to ensure teams understand expected outcomes and delivery priorities.
• Facilitate sprint planning, backlog refinement, product reviews, demonstrations, and release-readiness discussions.
• Coordinate with quality assurance teams to establish feature validation procedures and resolve product issues.
• Work with marketing, sales, customer success, and support teams to communicate feature capabilities, limitations, and customer benefits.
• Prepare product updates, decision documents, roadmap presentations, and performance reports for senior management.

Responsible AI, Risk Management, and Governance

• Promote responsible AI practices, including fairness, transparency, privacy, security, reliability, and appropriate human oversight.
• Collaborate with legal, security, compliance, and technical teams to identify risks associated with AI functionality.
• Ensure product requirements address data protection, access controls, information handling, and applicable organizational policies.
• Evaluate potential bias, inaccurate outputs, inconsistent responses, and other failure modes relevant to the product.
• Establish escalation and mitigation processes for critical AI feature failures and unexpected behavior.
• Maintain clear documentation of feature decisions, evaluation results, known limitations, and improvement plans.

Product Launch and Continuous Improvement

• Coordinate launch planning, release communications, internal training, and customer-facing documentation for new AI features.
• Define launch-readiness criteria and confirm that essential testing, monitoring, support processes, and documentation are completed.
• Monitor adoption, engagement, satisfaction, conversion, retention, and other relevant product metrics.
• Identify opportunities to improve product usability, reduce friction, and increase the practical value of AI capabilities.
• Lead post-launch reviews to assess whether features achieved their intended outcomes.
• Use performance insights and customer feedback to inform future roadmap decisions and continuous product improvement.

3. Required Skills and Qualifications

Educational Qualifications

• Bachelors degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, Business Administration, or a related discipline.
• A masters degree in product management, business administration, data science, machine learning, or a related field is an advantage but not mandatory.
• Relevant certifications in product management, agile methodologies, AI fundamentals, or data analytics are desirable.

Product Management Skills

• Strong understanding of product discovery, roadmap planning, feature prioritization, requirements gathering, and product lifecycle management.
• Ability to translate business objectives and customer needs into clear, actionable product requirements.
• Experience defining product KPIs, success metrics, acceptance criteria, and evaluation methodologies.
• Knowledge of agile product development, Scrum, Kanban, backlog management, and iterative delivery.
• Ability to balance customer value, commercial objectives, engineering constraints, and responsible AI considerations.
• Strong problem-solving, analytical thinking, and strategic decision-making skills.

AI and Technical Knowledge

• Working knowledge of artificial intelligence, machine learning, deep learning, and data-driven product development.
• Familiarity with generative AI concepts, large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and AI-assisted workflows.
• Understanding of AI evaluation methods, including accuracy, precision, recall, relevance, robustness, latency, and task-specific performance metrics.
• Familiarity with model evaluation datasets, benchmarking, human evaluation, and automated testing approaches.
• Awareness of model limitations, hallucinations, bias, data quality issues, and the importance of continuous monitoring.
• Basic understanding of APIs, data pipelines, cloud platforms, software development workflows, and system integrations.
• Ability to discuss technical trade-offs with engineering and data science teams without necessarily being responsible for writing production code.

Analytical and Research Skills

• Ability to interpret dashboards, product analytics, customer feedback, experimentation results, and business performance reports.
• Familiarity with A/B testing, hypothesis development, statistical significance, and evidence-based product decisions.
• Strong research capabilities, including competitor analysis, market assessment, customer discovery, and opportunity sizing.
• Ability to identify patterns, investigate performance gaps, and recommend practical solutions.
• Experience using data to prioritize initiatives and evaluate return on investment.

Communication and Leadership

• Excellent written and verbal communication skills.
• Ability to communicate complex AI concepts in clear, accessible language for business stakeholders and customers.
• Strong stakeholder management, negotiation, and collaboration skills.
• Confidence in facilitating product discussions, presenting recommendations, and resolving competing priorities.
• Ability to influence cross-functional teams without relying solely on formal authority.
• Strong organizational skills and the ability to manage multiple initiatives in a dynamic environment.

4. Experience

• Typically, 3–6 years of experience in product management, technical product management, digital product development, business analysis, or a related field.
• Experience managing technology products, software features, data-driven solutions, or AI-enabled applications is highly desirable.
• Previous involvement in feature discovery, roadmap development, product experimentation, or performance evaluation is preferred.
• Experience working with engineering, design, analytics, and business teams in an agile development environment.
• Familiarity with AI product evaluation, generative AI applications, model performance metrics, or machine learning workflows is an advantage.
• Demonstrated ability to define product requirements, manage backlogs, measure feature outcomes, and communicate results to stakeholders.
• Experience with product analytics platforms, experimentation tools, project management systems, or dashboarding solutions is beneficial.
• Candidates with strong transferable product management skills and a demonstrated interest in AI may also be considered for appropriately scoped opportunities.

5. Working Hours

Employment Schedule: Full-time, typically 40 hours per week.

Standard Working Hours: Generally aligned with the teams agreed business hours.

Work Arrangement: Remote, hybrid, or office-based, depending on the position and organizational requirements.

Collaboration: Participation in team meetings, sprint planning, product reviews, and stakeholder discussions is expected.

Flexibility: Some flexibility may be available to accommodate distributed teams and cross-functional collaboration.

Project Requirements: Occasional schedule adjustments may be necessary during product launches, testing cycles, or critical release periods.

The final schedule, time zone requirements, and work arrangement will be confirmed during the recruitment process.

6. Knowledge, Skills, and Abilities

Technical Knowledge

• Understanding of AI product development workflows, model capabilities, and common limitations.
• Knowledge of feature evaluation, performance measurement, product experimentation, and continuous monitoring.
• Familiarity with product analytics, data visualization, and reporting tools.
• Awareness of software development lifecycles, API integrations, cloud-based applications, and data governance principles.
• Understanding of privacy, security, responsible AI, and quality assurance considerations.

Strategic and Analytical Abilities

• Ability to identify high-impact product opportunities and prioritize them using structured decision-making frameworks.
• Capacity to connect customer needs, technical feasibility, business objectives, and measurable product outcomes.
• Ability to assess uncertainty, evaluate trade-offs, and make informed decisions using available evidence.
• Skill in interpreting complex information and converting insights into clear product recommendations.
• Ability to define meaningful experiments and determine whether a feature delivers sufficient value to justify further investment.

Interpersonal and Organizational Skills

• Strong collaboration and relationship-building capabilities.
• Excellent written documentation, presentation, and meeting facilitation skills.
• Ability to manage competing deadlines and maintain focus on strategic priorities.
• Confidence in working with diverse technical and non-technical stakeholders.
• Strong attention to detail when defining acceptance criteria, reviewing evaluation results, and documenting decisions.
• Adaptability and willingness to learn as AI technologies, customer expectations, and product requirements evolve.

Personal Attributes

• Customer-focused, curious, proactive, and results-oriented.
• Comfortable working in an evolving technology environment.
• Committed to ethical product development and continuous learning.
• Able to take ownership of initiatives while remaining open to feedback.
• Motivated by solving complex problems and delivering measurable improvements.

7. Benefits

• The Elite Job aims to offer a supportive and development-oriented working environment. Depending on the employment arrangement and applicable company policies, potential benefits may include:
• Competitive Compensation: Salary aligned with experience, role responsibilities, and relevant market benchmarks.
• Flexible Work Options: Remote or hybrid arrangements where supported by the position.
• Professional Development: Opportunities to expand knowledge of AI product management, machine learning, experimentation, and emerging technologies.
• Learning Resources: Access to relevant training materials, professional courses, workshops, and industry learning opportunities where available.
• Career Development: Opportunities to grow into senior product management, AI product leadership, or broader product strategy roles.
• Collaborative Environment: Opportunities to work with professionals across engineering, data science, design, analytics, and business functions.
• Meaningful Product Ownership: The opportunity to contribute to feature decisions, product roadmaps, and measurable customer outcomes.
• Performance Recognition: Recognition for contributions to product quality, innovation, customer satisfaction, and business results, subject to company policy.
• Employee Benefits: Any applicable leave, insurance, retirement, or other benefits will depend on the employment location and confirmed offer terms.
• Specific benefits, eligibility requirements, and compensation details will be communicated during recruitment and formal offer discussions.

8. Why Join The Elite Job?

Joining The Elite Job as an AI Product Manager – Feature Strategy and Evaluation offers an opportunity to contribute to the development of intelligent, customer-focused products in a rapidly evolving technology landscape.

This position is suited to professionals who want to combine product strategy, analytical thinking, customer research, and AI innovation. You will have the opportunity to influence which features are developed, how their success is measured, and how product experiences improve over time.

Key reasons to consider this opportunity include:

• Shape AI Product Strategy: Contribute to product vision, roadmap decisions, and the prioritization of valuable AI capabilities.
• Drive Evidence-Based Decisions: Use experimentation, evaluation frameworks, customer feedback, and product analytics to guide feature development.
• Collaborate Across Disciplines: Work with technical and business professionals to translate complex challenges into practical solutions.
• Focus on Customer Value: Help ensure that AI-powered features address genuine user needs and deliver measurable improvements.
• Build Future-Ready Skills: Deepen your understanding of AI product evaluation, generative AI, responsible AI, and modern product development.
• Influence Continuous Improvement: Help identify feature gaps, assess product performance, and recommend improvements throughout the product lifecycle.
• Develop Your Career: Build experience that can support progression toward senior product management, AI product strategy, or product leadership opportunities.

The Elite Job welcomes professionals who demonstrate sound judgment, a customer-first mindset, analytical ability, and a genuine interest in building useful and responsible AI products.