AI Product Manager

Responsibilities

• Own the end-to-end product lifecycle: from problem discovery and user research to release and feedback loops.
• Define product requirements and technical specifications for data ingestion, entity resolution, and linking workflows.
• Collaborate with engineers to translate business logic into scalable technical solutions.
• Prioritize features based on business value, customer feedback, and technical feasibility.
• Write user stories, product briefs, and technical documentation to ensure clarity across stakeholders.
• Understand customer datasets and workflows deeply to inform intelligent defaults and product design.
• Partner with QA and engineering to ensure features are testable, verifiable, and performant.
• Set and monitor success metrics for product adoption, accuracy, and speed.
• Manage internal and external feedback loops to drive continuous improvement.
• Stay close to the data work with JSON payloads, inspect APIs, and evaluate prototype outputs when needed.
• Design GTM strategy to expose the product commercially
• Research and innovation: Stay up-to-date with the latest advancements in machine learning and NLP, and explore new techniques and technologies to improve the linking processes.
• Deploy and maintain models in a production environment, monitoring their performance and ensuring their reliability.

Required Qualifications

• 7-10 years of experience in product management, with a strong focus on Machine Learning, Natural Language Processing (NLP) for information extraction and MLOps
• Proven experience leading end-to-end implementation of multiple Data Science, Machine Learning, and AI projects, from ideation to deployment.
• Hands-on experience driving adoption of Generative AI and Agentic AI solutions at the enterprise level, with a focus on real-world impact and scalability.
• Experience building or managing data-heavy products (e.g., ETL tools, analytics platforms, data enrichment APIs).
• Prior experience in entity resolution, data linking, or metadata management.
• Expertise in Python and related NLP libraries (e.g., spaCy, NLTK, Transformers, Hugging Face)
• Experience with Apache Spark or other distributed computing frameworks for large-scale data processing.
• AWS/GCP Cloud expertise, particularly in deploying and scaling ML pipelines for NLP tasks.
• Solid understanding of the Machine Learning model lifecycle, including data preprocessing, feature engineering, model training, evaluation, deployment, and monitoring, specifically for information extraction models.
• Experience with CI/CD pipelines for ML models, including automated testing and deployment.
• Docker & Kubernetes experience for containerization and orchestration.
• Version control system experience with Git, GitHub, or Azure DevOps.
• Excellent Problem-solving, Code Review and Debugging skills
• Software craftsmanship, adherence to Agile principles and taking pride in writing good code
• Techniques to communicate change to non-technical people

Whats In It For You

• A high-impact role where your work directly shapes the future of a data platform.
• Autonomy to define product priorities and technical direction.
• The opportunity to collaborate with a brilliant team solving hard, unsolved data problems.
• Exposure to both strategic and hands-on product building.
• A learning environment with rapid iteration, customer feedback, and shipping fast.