01Overview
Required Qualifications
Education
Bachelor's or Master's degree in Engineering, Computer Science, Industrial Engineering, Data Science, or related field.
Experience
7+ years of experience in Manufacturing, Industrial Operations, Digital Manufacturing, Industry 4.0, or Industrial Digital Transformation.
3+ years designing and deploying AI, Analytics, Machine Learning, or GenAI solutions.
Experience leading large-scale enterprise transformation programs.
Strong client-facing consulting and solution architecture experience.
Required Technical Skills
AI & Advanced Analytics
Generative AI and Large Language Models (OpenAI, Azure OpenAI, Anthropic, Gemini, etc.)
Agentic AI and Multi-Agent Architectures
Machine Learning and Predictive Analytics
Computer Vision
Time-Series Analytics
Knowledge Graphs (Neo4j preferred)
RAG Architecture and Vector Databases
Cloud & Data Platforms
Microsoft Azure AI Services
Azure Data Platform
Databricks
Microsoft Fabric
AWS or Google Cloud AI Platforms
Data Engineering and Data Governance
Industrial & Manufacturing Systems
MES (AVEVA, Siemens Opcenter, Rockwell, Dassault, SAP ME)
SCADA, DCS, PLC Ecosystems
Historians (PI, IP21, Canary)
ERP (SAP, Oracle)
PLM and Engineering Systems
IIoT Platforms
Architecture & Engineering
Enterprise Architecture
Solution Architecture
API and Integration Design
MLOps / LLMOps
AI Governance & Responsible AI
Cybersecurity for Industrial Environments
Preferred Qualifications
Experience in Manufacturing Copilots, Operations Copilots, or Engineering Assistants.
Experience with Digital Twin platforms and simulation technologies.
Certifications in Azure AI, Azure Solutions Architecture, AWS ML, or equivalent.
Experience working with industrial data platforms such as Cognite, AVEVA CONNECT, or DataHub platforms.
Familiarity with autonomous operations and closed-loop optimization systems.
Key Competencies
Strategic Thinking
Executive Communication
Consultative Problem Solving
Innovation Mindset
Cross-Functional Leadership
Architecture Governance
Manufacturing Domain Expertise
Stakeholder Management
Business Value Realization
Success Metrics
AI solutions successfully deployed into production.
Measurable client outcomes such as productivity improvement, cost reduction, quality enhancement, and operational efficiency gains.
Growth of AI-driven business pipeline and revenue.
Development of reusable assets and industrial AI accelerators.
Increased adoption of AI across manufacturing and industrial operations.
Client satisfaction and executive stakeholder trust.
Ideal Profile
A visionary technology leader who understands both the factory floor and the AI frontiercapable of transforming industrial operations through scalable, responsible, and outcome-driven AI solutions. .
