Lead AI Platform And Automation Engineer - GCP Vertex AI IBM Watsonx MLFlow Terraform

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Job Description

Job Summary

We are looking for a strategic and hands-on Lead AI Platform & Automation Engineer to own and scale our enterprise AI platform capabilities. This role will serve as the primary owner of the IBM watsonx platform and Google Cloud Vertex AI platform, while providing support to Azure driving adoption, standardization, automation, and governance across the organization. The role also includes leading a team responsible for day-to-day AI model and application onboarding, pipeline automation, DevSecOps integration, BI tooling, and AI governance—including maintenance of IBM watsonx.governance and vertex AI.

This is a high-impact position suited for a technically strong leader who can lead technical team, architect solutions, scale platforms, and align multiple teams across engineering, data science, and business domains.

Key Responsibilities

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.
  
  

• 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.
  
  

• 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.
  
  

• 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.
  
  

• 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.
  
  

• 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.
  
  

• 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.
  
  

• Bachelor’s or master’s in computer science, Engineering, or related technical field.
  
  

• 8+ years in cloud engineering, MLOps, or platform leadership roles.
• 3+ years leading enterprise platform delivery and infrastructure automation.
• Proven experience managing IBM watsonx and Vertex AI in production environments.
• Hands-on delivery of secure, automated pipelines using Terraform, GitOps, and containerized workflows.
• Experience implementing AI governance and compliance using commercial tools and internal frameworks.
• Familiarity with BI tools (Power BI, Looker) and semantic modeling techniques.
  
  

• Platforms: IBM watsonx.ai, watsonx.governance, Vertex AI, Azure ML
• Automation: Terraform, Ansible, GitHub Actions, GitLab CI, Azure DevOps
• Languages: Python, Bash, YAML
• Containers: Docker, Kubernetes
• ML Tools: MLflow, TFX, or similar
• Governance: Bias detection, metadata tracking, model lineage, risk management
  
  

• Certifications: GCP, IBM, or Azure AI/MLOps certifications
• Experience with Agentic AI systems (e.g., LangChain, IBM Orchestrate, or custom frameworks)
• Familiarity with NIST, EU AI Act, or internal model risk governance frameworks
• Prior experience deploying internal platform-as-a-service offerings for AI/ML