Manager – Strategic Sourcing (Digitization & Analytics)

Key Responsibilities-

AI & ML Strategy for Strategic Sourcing

• Partner with Purchasing, Commodity Teams, Supplier Quality, Cost Engineering, and Strategic Sourcing stakeholders to identify AI/ML use cases and business transformation opportunities.
• Understand and map procurement processes, including RFQ management, supplier evaluation, spend analytics, cost optimization, contract analytics, demand forecasting, supplier risk monitoring, and procurement operations automation.
• Define an AI roadmap aligned with organizational digital transformation goals.
• Build scalable AI frameworks and reusable AI components for sourcing functions.

AI/ML Solution Development

• Design, develop, and deploy AI/ML applications for procurement and sourcing functions.
• Develop predictive, prescriptive, and Generative AI solutions using structured and unstructured enterprise data.
• Build intelligent copilots and AI assistants for procurement users using LLM technologies.
• Implement NLP models, recommendation systems, forecasting models, supplier intelligence engines, semantic search systems, document intelligence solutions, and Generative AI workflows.
• Create AI-enabled dashboards and analytical insight platforms.

LLM & Generative AI

• Architect and implement enterprise-grade LLM applications.
• Fine-tune and optimize LLMs for procurement-specific use cases.
• Build Retrieval-Augmented Generation (RAG) frameworks using enterprise data sources.
• Integrate AI copilots with sourcing workflows and enterprise systems.
• Develop prompt engineering frameworks and AI governance standards.
• Evaluate open-source and commercial Generative AI ecosystems for enterprise adoption.
• Ensure responsible AI, security, compliance, and data governance.

Enterprise Systems Integration

• Lead integration of AI applications with enterprise platforms, including SAP S/4HANA, Qlik SaaS, 3DEXPERIENCE (3DX), PLM systems, supplier portals, and internal analytics platforms.
• Develop API-based architectures and middleware integrations.
• Collaborate with IT, Digital, Data Engineering, and Cloud teams for enterprise deployment.
• Ensure scalability, cybersecurity, reliability, and performance optimization.

Full Stack & Application Development

• Lead the development of AI-powered web applications and user interfaces.
• Build scalable front-end solutions using React.
• Develop backend AI services and APIs using Python.
• Implement cloud-native and microservices-based architectures.
• Drive DevOps and MLOps best practices for continuous deployment and monitoring.

Stakeholder & Team Management

• Work cross-functionally with sourcing leaders, business users, digital teams, and external partners.
• Conduct workshops to identify automation and AI opportunities.
• Mentor data scientists, AI engineers, and analysts.
• Drive vendor evaluations and technology partnerships.

Education Qualification

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, Electronics Engineering, or a related field.
• MBA or specialization in Supply Chain, Analytics, or AI is preferred.

Experience

• 8–15 years of experience in AI/ML, analytics, or enterprise digital transformation.
• Strong experience in manufacturing, automotive, procurement, or supply chain environments is preferred.
• Proven track record of delivering enterprise AI applications.

Technical Skills

Core AI & ML

• Expert-level Python programming.
• Strong expertise in Machine Learning, Deep Learning, NLP, Generative AI, and LLM architectures.
• Hands-on experience with LangChain, vector databases, RAG pipelines, prompt engineering, and AI orchestration frameworks.
• Experience with TensorFlow, PyTorch, and Scikit-learn.

Enterprise Systems

• Strong knowledge of SAP S/4HANA, Qlik SaaS, and 3DEXPERIENCE (3DX).
• Experience with enterprise data lakes, API integrations, and middleware systems.

Development Skills

• Strong React development skills.
• Expertise in REST APIs and microservices architecture.
• Experience with FastAPI/Flask, Docker, Kubernetes, CI/CD pipelines, and MLOps platforms.
• Experience with cloud platforms such as Azure, AWS, or GCP.

Data & Analytics

• Strong SQL and enterprise data modeling skills.
• Experience handling large-scale enterprise datasets.
• Experience with dashboarding and analytical platform integration.

Preferred Industry Knowledge

• Automotive purchasing and sourcing processes.
• Understanding of BOM and cost structures.
• Supplier ecosystem management.
• Manufacturing digital transformation.
• Procurement analytics and spend management.

Behavioral Competencies

• Strong problem-solving and analytical mindset.
• Excellent stakeholder management skills.
• Strong communication and presentation skills.
• Ability to translate business problems into AI-driven solutions.
• Innovation-oriented with a strong execution focus.
• Ability to work effectively in fast-paced, cross-functional environments.