AI Engineer

Platform Leadership 

• Serve as the lead owner of IBM watsonx and Google Vertex AI platforms, overseeing configuration, governance, and operational maturity. 

• Drive platform onboarding strategy, enablement, and hands-on support across data science, engineering, and analytics teams. 

• Standardize reusable frameworks, templates, and infrastructure patterns for both platforms to accelerate project delivery. 

AI & ML Pipeline Automation 

• Architect and manage enterprise-wide CI/CD and MLOps pipelines for: 

• Model training, tuning, evaluation, and deployment 

• Data ingestion, transformation, and streaming 

• Infrastructure provisioning and teardown (IaC) 

• Build and maintain reusable templates for AI applications, workflows, and serving endpoints in IBM watsonx.ai and Vertex AI. 

Agentic AI Enablement 

• Design and operationalize pipelines for Agentic AI systems using LLMs, orchestration agents, and decision engines. 

• Integrate intelligent agent workflows with platform capabilities and monitor lifecycle behavior and governance adherence. 

AI Governance (incl. watsonx.governance) 

• Implement and operationalize AI governance frameworks using IBM watsonx.governance. 

• Define model approval workflows, track metadata, enforce responsible AI policies, and ensure transparency, bias detection, and explainability. 

• Collaborate with legal, compliance, and information security teams to embed AI governance across all AI/ML systems. 

DevSecOps & Automation 

• Integrate DevSecOps practices into pipelines—automating vulnerability scans, access policies, and secrets management. 

• Embed security compliance and quality gates into all CI/CD workflows across AI and application domains. 

Business Intelligence & Semantic Modeling 

• Provide architectural guidance to BI teams using Power BI and Looker. 

• Oversee data modeling, semantic layer standardization, and connectivity to enterprise data lakes and warehouses. 

Team Leadership 

• Lead and mentor a cross-functional team of AI platform engineers, MLOps practitioners, BI analysts, and DevSecOps engineers. 

• Define goals, delivery milestones, documentation standards, and best practices for scalable operations.