Senior Process Engineer Lead – AIML Driven Transformation
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Monthly Salary:
Posted on:
3 days ago
Posted on:
Vacancies:
1 Vacancy
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Job Summary
Senior Process Engineer / Lead AI/ML Driven Transformation
Experience: 12 15 Years Role Summary
Own enterprise-scale AI/ML-led process transformation driving predictive autonomous and intelligent operations across functions.
Key Responsibilities
Define and lead the AI-driven process excellence roadmap across business units
Identify high-impact opportunities for advanced analytics ML and GenAI in core processes
Architect scalable solutions involving predictive control optimization and decision intelligence
Lead cross-functional teams (Process Data Science IT Business)
Establish standards for model governance explainability MLOps and lifecycle management
Mentor engineers on AI-first process design and capability building
Build business cases quantify ROI and present outcomes to senior leadership
Define and lead the AI-driven process excellence roadmap across business units
Identify high-impact opportunities for advanced analytics ML and GenAI in core processes
Architect scalable solutions involving predictive control optimization and decision intelligence
Lead cross-functional teams (Process Data Science IT Business)
Establish standards for model governance explainability MLOps and lifecycle management
Mentor engineers on AI-first process design and capability building
Build business cases quantify ROI and present outcomes to senior leadership
Mandatory Skills & Experience Requirements
Process Engineering Leadership: 10 12 years (enterprise process transformation Lean/Six Sigma at scale)
Advanced Analytics / AI/ML Systems: 6 8 years designing and deploying ML solutions
MLOps & Model Lifecycle Management: 4 6 years (deployment monitoring drift governance)
Python & AI Engineering: 6 8 years (ML pipelines feature engineering production-grade code)
Cloud & Data Platforms: 5 7 years (Azure ML Databricks data pipelines scalable architectures)
Production AI Deployment: 4 6 years delivering AI solutions used by operations/business teams
Stakeholder & Change Leadership: 5 7 years driving transformation with senior leadership engagement
Process Engineering Leadership: 10 12 years (enterprise process transformation Lean/Six Sigma at scale)
Advanced Analytics / AI/ML Systems: 6 8 years designing and deploying ML solutions
MLOps & Model Lifecycle Management: 4 6 years (deployment monitoring drift governance)
Python & AI Engineering: 6 8 years (ML pipelines feature engineering production-grade code)
Cloud & Data Platforms: 5 7 years (Azure ML Databricks data pipelines scalable architectures)
Production AI Deployment: 4 6 years delivering AI solutions used by operations/business teams
Stakeholder & Change Leadership: 5 7 years driving transformation with senior leadership engagement
Success Metrics
Enterprise-level cost savings and productivity gains
AI adoption maturity across the process landscape
Repeatable AI/process frameworks and standards established
Enterprise-level cost savings and productivity gains
AI adoption maturity across the process landscape
Repeatable AI/process frameworks and standards established
Optional Add-Ons (for both levels)
Industry 4.0 / Smart Factory exposure
Digital twin simulation or GenAI for process documentation and decision support
Certifications: Lean Six Sigma AI/ML Cloud
Industry 4.0 / Smart Factory exposure
Digital twin simulation or GenAI for process documentation and decision support
Certifications: Lean Six Sigma AI/ML Cloud
