AI PRODUCT ENGINEER

Job Description

Key Responsibilities

Define and execute the product vision, strategy, and roadmap for AI-powered products.
Identify customer needs and translate them into clear product requirements.
Work with data scientists and ML engineers to define AI/ML use cases and success metrics.
Prioritize product features based on customer value, business impact, technical feasibility, and risk.
Create PRDs, user stories, product specifications, and acceptance criteria.
Lead products through the full lifecycle—from discovery and experimentation to launch and continuous improvement.
Define and track KPIs such as adoption, engagement, accuracy, conversion, retention, and ROI.
Collaborate with engineering, design, data science, legal, security, and business teams.
Evaluate AI models and products for quality, reliability, bias, safety, privacy, and cost.
Stay current with developments in Generative AI, LLMs, RAG, AI agents, prompt engineering, and ML platforms.
Conduct customer research, competitor analysis, and market research.
Communicate product strategy, progress, risks, and results to senior leadership and stakeholders.

Required Qualifications:

2–5+ years of experience in product management, preferably in AI/ML, SaaS, technology, or data products.
Strong understanding of AI/ML concepts and the AI product development lifecycle.
Experience working with engineering and data science teams.
Strong analytical, problem-solving, and decision-making skills.
Experience defining product roadmaps, requirements, and KPIs.
Excellent communication and stakeholder-management skills.
Ability to translate complex technical concepts into simple business and customer outcomes.

Preferred Skills:
Experience with Generative AI and Large Language Models (LLMs).
Understanding of RAG, vector databases, embeddings, AI agents, fine-tuning, and prompt engineering.
Experience using AI APIs and platforms such as OpenAI, Anthropic, Google, AWS, or Azure.
Basic knowledge of SQL, Python, analytics tools, or experimentation frameworks.
Experience building B2B SaaS or consumer AI products.
Understanding of responsible AI, privacy, security, and AI governance.

Key Success Metrics:
Product adoption and user engagement
Customer satisfaction and retention
AI/model quality and accuracy
Business revenue or cost savings
Product delivery speed
Experimentation and conversion rates
AI infrastructure/model cost efficiency