GenAI Product Manager

Role Overview: You will be a part of a strong and growing technical team in a major FMCG organization, specializing in household products. Your main responsibilities will include owning the end-to-end MLOps architecture for AI platforms, translating business needs into AI solutions, embedding ML/GenAI into enterprise systems, establishing AI governance frameworks, defining multi-year AI roadmaps, and serving as a product owner for AI platforms. Key Responsibilities: - Own end-to-end MLOps architecture for AI platforms, including model development, deployment, monitoring, retraining, CI/CD, versioning, experiment tracking, and observability - Translate business needs into predictive, optimization, and generative AI solutions, lead full lifecycle delivery of AI applications, and design agentic systems with human-in-the-loop controls - Embed ML/GenAI into enterprise systems via API-first, cloud-native architectures, lead on-prem/private LLM hosting, and oversee reliability engineering and technical debt management - Establish AI governance frameworks around lifecycle management, compliance, auditability, bias mitigation, privacy, and responsible AI practices - Serve as a product owner for AI platforms, define multi-year AI roadmaps aligned to business goals, communicate AI strategy and progress to senior stakeholders, and champion organization-wide AI adoption Qualifications Required: - 8-12 years of experience in AI/ML engineering, MLOps, or AI platform leadership roles - Proficiency in MLOps & ML Lifecycle, Tools & Frameworks (MLflow, Airflow, AzureML, etc.), On-Prem LLM Hosting & Inference, Agentic AI frameworks, AI/ML System Design, Generative AI/LLM Architectures, Cloud & Data Engineering, and Databases - Strong skills in technology product management, senior stakeholder engagement, executive communication, and AI governance frameworks - Demonstrated managerial and leadership competencies in strategic thinking, multi-year technology roadmap development, risk assessment and mitigation, cross-functional stakeholder management, financial and vendor management, team building, mentoring, and driving AI adoption at scale (Note: No additional details of the company were provided in the job description) Role Overview: You will be a part of a strong and growing technical team in a major FMCG organization, specializing in household products. Your main responsibilities will include owning the end-to-end MLOps architecture for AI platforms, translating business needs into AI solutions, embedding ML/GenAI into enterprise systems, establishing AI governance frameworks, defining multi-year AI roadmaps, and serving as a product owner for AI platforms. Key Responsibilities: - Own end-to-end MLOps architecture for AI platforms, including model development, deployment, monitoring, retraining, CI/CD, versioning, experiment tracking, and observability - Translate business needs into predictive, optimization, and generative AI solutions, lead full lifecycle delivery of AI applications, and design agentic systems with human-in-the-loop controls - Embed ML/GenAI into enterprise systems via API-first, cloud-native architectures, lead on-prem/private LLM hosting, and oversee reliability engineering and technical debt management - Establish AI governance frameworks around lifecycle management, compliance, auditability, bias mitigation, privacy, and responsible AI practices - Serve as a product owner for AI platforms, define multi-year AI roadmaps aligned to business goals, communicate AI strategy and progress to senior stakeholders, and champion organization-wide AI adoption Qualifications Required: - 8-12 years of experience in AI/ML engineering, MLOps, or AI platform leadership roles - Proficiency in MLOps & ML Lifecycle, Tools & Frameworks (MLflow, Airflow, AzureML, etc.), On-Prem LLM Hosting & Inference, Agentic AI frameworks, AI/ML System Design, Generative AI/LLM Architectures, Cloud & Data Engineering, and Databases - Strong skills in technology product management, senior stakeholder engagement, executive communication, and AI governance frameworks - Demonstrated managerial and leadership competencies in strategic thinking, multi-year technology roadmap development, risk assessment and mitigation, cross-functional stakeholder management, financial and vendor management, team building, mentoring, and driving AI adoption at scale (Note: No additional details of the company were provided in the job description)