Product Manager-AI

Job Summary

AI / ML SAAS Product Development Manager

Key Responsibilities

Leadership & Strategy

● Pioneer AI/ML research and prototyping 

● Lead, mentor, and manage a team of backend, frontend, and mobile developers.

● Create an environment of engineering excellence, innovation, and high accountability.

● Align development efforts with business objectives, delivery timelines, and long-term architecture goals 

Technical Ownership

● Provide architectural direction and oversee high-level design decisions.

● Review and guide code quality, performance, scalability, and security standards.

● Collaborate with Product Managers and UX Designers to shape the product roadmap.

● Translate business needs into modular, configurable product features.

● Lead initiatives to build SaaS-ready AI architecture with multi-tenancy, usage metering, and integration APIs. 

● Work closely with product owners, UX teams, and QA to plan, estimate, and deliver product features. 

● Ensure best practices in Agile development, DevOps, and continuous deliveries

Hands-On Contribution

● Participate in key coding tasks, AI/ML POCs, and architectural refactoring

● Debug and resolve complex technical issues in backend and frontend layers.

Core Technical Skill Sets

Backend / Core

● Java (12+ years), Spring, Spring Boot, Hibernate/JPA, RESTful APIs

● Microservices architecture and integration patterns 

● SQL (MySQL, PostgreSQL), NoSQL (MongoDB, Redis) 

● CI/CD (Jenkins, GitLab CI) 

● Cloud: AWS / Azure / GCP 

Frontend

● HTML5, CSS3, JavaScript, JSP 

● jQuery and Bootstrap for legacy and lightweight frontend use 

● ReactJS for modern web applications 

● REST integration with UI, cross-browser compatibilities 

Mobile / Cross-Platform

● React Native for hybrid mobile application development 

AI/ML & Data Engineering

● Familiarity with Python for AI/ML model integration 

● Basic understanding of machine learning workflows (scikit-learn, TensorFlow, PyTorch)

● Ability to lead integration of ML models into production environments

● Identify use cases where AI/ML can be embedded for customer value (e.g., recommendations, predictions) 

● Prototype and test AI-driven features in production environments with safety and observability.