Role: Lead
Required Skills: Artificial Intelligence | Machine Learning
Experience: 10 - 15 Years
Function: TECHNOLOGY
Apply By: 2026-12-21
Must Have Technical/Functional Skills
- Strong AI/ML leadership with hands-on depth in manufacturing and supply chain analytics, from use-case discovery through production adoption.
- Advanced machine learning and data science using Python, scikit-learn, PyTorch or TensorFlow, including feature engineering, model selection, evaluation, explainability and optimization.
- Proven delivery of manufacturing use cases such as predictive maintenance, anomaly detection, quality prediction, yield improvement, process optimization, computer vision inspection or energy optimization.
- Proven delivery of supply chain use cases such as demand forecasting, inventory optimization, supply risk sensing, production planning, logistics optimization, order fulfillment or control-tower analytics.
- Experience with time-series forecasting, optimization, simulation and machine learning techniques applied to operational data.
- Experience integrating enterprise and industrial data from ERP, MES, WMS, TMS, planning platforms, IoT, sensor, historian and streaming sources.
- Production-scale MLOps: MLflow, model serving and monitoring, feature stores, drift management, CI/CD, Docker and Kubernetes.
- Expertise in at least one cloud AI and data stack: Google Cloud, Azure or AWS; familiarity with Databricks and/or Snowflake is desirable.
- Understanding of modern data architecture, lakehouse patterns, Spark/PySpark, SQL, streaming, data quality, lineage and governance.
- Working knowledge of GenAI and agentic patterns for manufacturing and supply chain knowledge assistance, root-cause analysis, operational decision support and workflow automation.
- Ability to facilitate customer workshops, shape solution architectures, build business cases and communicate with operations, supply chain, engineering, data and executive stakeholders.
- Experience leading distributed onsite-offshore teams and taking AI/ML initiatives from PoC through pilot to production.
Roles & Responsibilities
- Lead the manufacturing and supply chain AI/ML capability for the Data & Analytics unit, combining domain-led consulting with hands-on solution leadership.
- Engage manufacturing, operations, planning, logistics, procurement and technology leaders to identify problems, assess data readiness and prioritize high-value AI/ML use cases.
- Facilitate discovery and design workshops, define target architectures and convert opportunities into a practical roadmap with measurable operational outcomes.
- Architect solutions for predictive maintenance, quality, yield, forecasting, inventory, production planning, logistics and supply-risk analytics based on customer priorities.
- Guide data preparation, feature engineering, model development, validation, deployment, monitoring and continuous improvement.
- Ensure solutions integrate securely with enterprise, manufacturing and supply chain platforms and operate reliably within production workflows.
- Own or contribute to proactive proposals, RFP/RFI responses, estimates, delivery models, demonstrations and executive presentations.
- Lead PoCs and pilots through production, incorporating MLOps, explainability, responsible AI, monitoring, cost control and governance.
- Coordinate planning, staffing, technical reviews and execution governance with offshore engineering, data science and CoE teams.
- Build reusable accelerators, reference architectures and solution assets for manufactu ring and supply chain analytics.
- Mentor data scientists, ML engineers and architects, establish technical standards and support opportunity qualification with account leadership.
Generic Managerial Skills, If any
- Ability to lead a specialized AI/ML capability across accounts and teams without relying solely on formal authority.
- Strong consultative communication with plant, operations, supply chain, engineering, technology and executive stakeholders.
- Ability to translate business and operational challenges into scalable analytics and AI solutions with clear value measures.
- Experience coordinating distributed onsite-offshore teams, mentoring technical talent and governing delivery quality.
- Self-directed, pragmatic and comfortable building a new capability in an evolving unit; willing to travel to customer locations as required.
Key Words to search in Resume
AI/ML Lead; Manufacturing Analytics; Supply Chain Analytics; Predictive Maintenance; Anomaly Detection; Quality Prediction; Yield Optimization; Process Optimization; Computer Vision; Demand Forecasting; Inventory Optimization; Supply Risk; Production Planning; Logistics Optimization; Control Tower Analytics; Time-Series Forecasting; Operations Research; Optimization; Simulation; Python; PyTorch; TensorFlow; scikit-learn; MLOps; MLflow; Model Monitoring; Feature Store; Responsible AI; Explainable AI; ERP; MES; WMS; TMS; IoT; Historian; Kafka; Spark; PySpark; Databricks; Snowflake; Vertex AI; Azure ML; AWS SageMaker; GenAI; Agentic AI; PoC to Production; Onsite-Offshore Delivery
Pre-Screening Questionnaire
- Describe an AI/ML solution you led for manufacturing or supply chain. What business problem, data, model, architecture and measurable outcome were involved?
- Which manufacturing use cases have you delivered, such as predictive maintenance, quality, yield, process optimization or computer vision?
- Which supply chain use cases have you delivered, such as forecasting, inventory, planning, logistics, fulfillment or supply-risk analytics?
- How do you select and validate forecasting, machine learning, optimization or simulation approaches for operational decisions?
- Describe an AI/ML initiative you took from PoC to production. How did you manage MLOps, monitoring, explainability, adoption and value realization?
- Tell us about a customer discovery or solution workshop you led with manufacturing or supply chain stakeholders and what it produced.
- Have you led distributed onsite-offshore teams or built reusable assets and offerings for manufacturing and supply chain analytics?
- What is your experience with cloud AI/data platforms, Databricks or Snowflake, and production-scale ML engineering?
- Are you Bay Area based or willing to relocate and travel regularly? What is your work authorization status?
Salary Range: $180,000 - $220,000 a year
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TCS Employee Benefits Summary:
Discretionary Annual Incentive.
Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
Family Support: Maternal & Parental Leaves.
Insurance Options: Auto & Home Insurance, Identity Theft Protection.
Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
Time Off: Vacatio n, Time Off, Sick Leave & Holidays.
Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.