Associate Director - AI Product Manager

About the Role

Title

AI Product Manager

This is a hands-on role for someone who can write technical specs, prototype solutions, and collaborate closely with engineers while keeping a sharp eye on product-market fit and user experience. You'll be at the forefront of a rapidly evolving field, learning and growing alongside some of the brightest minds in the industry. If you're passionate about AI, driven to make an impact, and thrive in a dynamic and supportive workplace, we encourage you to join us!

The Team

You’ll work alongside a lean but high-performing team of developers, QA, and data experts building a modern data platform. Our mission is to automate how data from multiple sources is linked and consumed, enabling clients to eliminate duplication, reduce manual work, and drive insight.

You’ll be the key driver of product thinking and delivery — guiding what we build, why we build it, and how we scale it.

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

What’s 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.