Product Manager - Central Data Repository & AI/Automation

Product Management at InMobi: We offer an opportunity to have immediate impact with the company and our products. You shall be working at the cutting edge of technology connecting various businesses at InMobi centrally. The work that you shall do will be mission critical for InMobi and is in direct path for success of multiple of core product offerings in InMobi. Our Product Managers in the CDR Product Team have a strong understanding of the company s underlying business, data, product and process challenges in reporting frameworks, translating them into long term sustainable product solutions that are applicable acro ss InMobi s expanding diversified bus iness, finance strategy units. This team will work across a spectrum of enterprise systems and internal products building end to end features an d capabilities that help establish a fully integrated blueprint of company s data architecture and system landscape. Our CDR Product Manager will need to go deep and understand business operations across the End-to-end value chain covering Lead generation, Sales CRM, Order fulfilment, Reporting Finance to identify product modifications, enhancements and system integrations keeping the larger inter-connected ecosystem in mind. They need to be extremely agile and on top of technology. They need to think big, multi-task and be able to deal with ambiguity with a positive attitude, in fact, thrive in it to deliver positive timely outcomes. In the role of an CDR AI Product Manager for Central Data Repository Product Team, responsibilities include however not limited to the following: Core AI Technical Skills Foundational ML Literacy: Deep understanding of supervised, unsupervised, and reinforcement learning, neural networks, and Natural Language Processing (NLP). Model Lifecycle Management (MLOps): Proficiency in managing the unique lifecycle of AI products, including data collection, model training, validation, deployment, and ongoing monitoring for model drift. Modern AI Architectures: Familiarity with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, and multimodal systems. AI Prototyping: Ability to use low-code/no-code tools (e.g., v0.dev, Lovable, or Gradio) to build functional AI prototypes for rapid validation Data Analytical Competencies Data Strategy Literacy: Understanding data pipelines, quality, annotation, and storage, alongside identifying gaps or biases in training datasets. Advanced Model Evaluation: Mastery of probabilistic metrics such as precision, recall, F1-score, and ROC-AUC to measure model performance beyond standard business KPIs. Context Engineering: Expertise in designing how AI systems interpret user intent by managing retrieval logic and memory layers Strategic Leadership Skills Ethical AI Governance: Prioritizing fairness, transparency, and accountability; managing compliance with emerging regulations like GDPR or the EU AI Act. Translational Communication: Acting as the "translator" between data science, engineering, and non-technical stakeholders to align technical constraints with business goals. Adaptive Product Strategy: Transitioning from deterministic "feature roadmaps" to adaptive learning loops that prioritize product-market fit based on real-time behavior. Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.