AI / Data Product Manager
The AI Product Manager guides the strategy and delivery of products powered by machine learning and generative models. Unlike traditional software PMs, they must manage probabilistic behavior, handle output ambiguity, and balance inference costs with product value.
The AI Product Manager guides the strategy and delivery of products powered by machine learning and generative models. Unlike traditional software PMs, they must manage probabilistic behavior, handle output ambiguity, and balance inference costs with product value.
Strong candidates understand model capabilities (LLMs, multimodal architectures, RAG), establish clear evaluation frameworks (Evals, precision, latency), and align technical teams with genuine customer problems.
Methodologies & technical foundations (% penetration)
Infrastructure & tooling demanded in job specs (% penetration)
Seniority Level Breakdown
Corporate hierarchy distribution
Top Hiring Enterprises
Leading corporations posting this role
- Veeva Systems18 positions
- Databricks8 positions
- Scale AI8 positions
- Workday, Inc.7 positions
- Datadog7 positions
- MongoDB5 positions
S&P 500 Sector Distribution
Industry sectors generating the highest hiring demand
- Information Technology33.3%
- Health Care19.3%
- Communication Services12.7%
- Financials12.7%
- Industrials8%
Live Enterprise Job Postings
Sample of active postings directly from corporate ATS boards
Staff Technical Product Manager, Ads ML Platform
Principal Product Manager - AI/ML
Technical Product Manager (AI) - Veeva QualityDocs & Training
Senior Program Manager, NPI Solutions – AI Infrastructure
Senior Technical Product Manager, Master Data Management (MDM)
Product Manager, AI Solutions
Staff Data Center Systems Product Manager
Senior Product Manager, Data Center Applications
Recommended Skill Tracks & Certifications
Curated programs on Coursera, DataCamp and cloud providers aligned with these role requirements
Google Cloud Professional Data Engineer Specialization
Official curriculum by Google Cloud covering large-scale data processing with BigQuery, streaming pipelines with Dataflow, and modern lakehouse architecture.
Data Engineer with Python Career Track
Interactive, browser-based coding track covering robust data pipelines, PostgreSQL, unit testing, software design patterns for data, and PySpark fundamentals.
Generative AI with Large Language Models
Created by Andrew Ng and AWS: practical engineering foundations of transformer architectures, pre-training, instruction fine-tuning, and alignment evaluation in production.
Google Cloud Professional Data Engineer
Gold standard certification assessing design, security, scalability, and operational reliability of BigQuery and GCP analytical pipelines.
Databricks Certified Data Engineer Professional
Validates mastery of streaming, batch ETL, Unity Catalog governance, and production Lakehouse architectures with Delta Lake and Spark.