Description :

- Immediate Joiners or less than 30 days notice period

Lead the planning, execution, and delivery of transformative AI projects in Energy solutions, engineering, manufacturing, retail, and supply chain optimization. You will manage cross-functional teams building agentic AI systems, computer vision solutions, generative AI applications, and workflow automations using paid and open-source tools (e.g., n8n Enterprise, LangChain, Meta Llama). Focus on agile delivery, ethical AI, stakeholder alignment, and measurable business impact such as revenue uplift, cost saving, and productivity & yield improvement.

Key Responsibilities :

- Define project scope, timelines, budgets, and success metrics for AI initiatives (e.g., predictive maintenance agents, RAG-based proposal systems).

- Coordinate with Data Scientists, AI Engineers, MLOps, and Developers for end-to-end delivery from ideation to production deployment.

- Implement Agile/Scrum methodologies, manage risks, and ensure compliance with data privacy and AI ethics standards.

- Track progress via KPIs (e.g., model accuracy, ROI) and report to senior leadership.

- Facilitate integration of open-source tools (n8n workflows, edge devices like NVIDIA H200x, Cloud services) and vendor collaborations.

Required Skills & Experience :

Project & AI/ML Management :

- 10+ years in project management (PMP or Agile certification preferred)

- 5+ years managing AI/ML projects in manufacturing or industrial domains.

- Strong understanding of next-gen AI concepts : agentic AI, generative AI (RAG, fine-tuning), computer vision, and MLOps pipelines.

Technical & Architecture :

- Experience with API-driven and event-driven architectures as well as edge computing for industrial environments.

- Strong understanding of cloud computing (IaaS, PaaS, containerization, microservices).

AI Governance :

- Define and implement AI governance frameworks : model approval processes, bias, explainability, and transparency standards.

- Data privacy and security controls across the AI lifecycle.

Leadership & Stakeholder Management :

- Excellent stakeholder management and cross-functional leadership skills.

- Ability to align diverse teams across Data Science, Engineering, MLOps, and Business units.

Preferred :

- Exposure to industrial use cases : predictive maintenance, inventory optimization, quality inspection.

- Knowledge of 2026 trends : multimodal AI, autonomous agents, real-time analytics.