Job description
Required Qualifications Education Bachelor's or Master's degree in Engineering, Computer Science, Data Science, Information Systems, Supply Chain, Operations Management, or a related field. Experience 5+ years of experience in Process Engineering, Automation, Digital Transformation, or Operations Excellence. 3+ years of experience in AI/ML, Generative AI, Agentic AI, or intelligent automation solutions.
Experience in Supply Chain, Manufacturing, Logistics, Operations, or Enterprise Transformation programs preferred. Proven track record of delivering AI-enabled business outcomes at scale. Technical Skills AI & Agentic Technologies Generative AI and Large Language Models (LLMs) Agentic AI frameworks (Auto. Gen, Microsoft Agent Framework, CrewAI, Lang.
Graph, Google ADK, etc.) Prompt Engineering and AI Evaluation Frameworks Retrieval-Augmented Generation (RAG) Vector Databases and Embedding Models Development & Integration Python and API Integration AI Orchestration Platforms Data Pipelines and Workflow Automation Enterprise System Integration (ERP, SCM, CRM) Observability & Analytics AI Telemetry and Monitoring Power BI, Tableau, or equivalent analytics platforms Performance optimization and operational dashboards Tools Jira, Azure DevOps, Confluence Azure AI Services, OpenAI, Amazon Bedrock, or similar AI platforms Cloud platforms (Azure, AWS, GCP) Preferred Certifications Generative AI or AI/ML Certifications Lean Six Sigma Green Belt or Black Belt APICS / Supply Chain Certifications Cloud Certifications (Azure, AWS, Google Cloud) Process Automation or Digital Transformation Certifications Key Competencies Systems Thinking and Structured Problem Solving Strong Analytical and Critical Thinking Skills Process Re-engineering and Continuous Improvement Mindset Stakeholder Management and Executive Communication Business Process Transformation AI Strategy and Innovation Leadership Cross-functional Collaboration and Change Managemen Success Metrics Increased AI adoption and automation across business processes Improvement in agent accuracy, reliability, and task completion rates Reduction in manual effort and process cycle times Enhanced supply chain and operational performance metrics Improved AI governance, observability, and compliance adherence Increased retrieval accuracy and response quality in RAG implementations Optimized AI operating costs and platform efficiency Measurable business value through AI-enabled transformation initiatives