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Job Description:

We are seeking a Senior MLOps / AIOps Platform Engineer with deep DevSecOps expertise and hands-on experience managing enterprise-grade AI/ML platforms. This critical role focuses on building, configuring, and operationalizing secure, scalable, and reusable infrastructure and pipelines that support AI and ML initiatives across the enterprise. The ideal candidate will have a strong background in Infrastructure as Code (IaC), pipeline automation, and platform engineering, with specific experience configuring and maintaining IBM watsonx and Google Cloud Vertex AI environments.

Platform Engineering and Support 

Serve as a lead platform engineer responsible for provisioning, configuring, and maintaining the IBM watsonx and Google Cloud Vertex AI environments. 

Ensure platforms are production-ready, secure, cost-effective, and performant across training, inferencing, and orchestration workflows. 

Manage updates, patching, integrations, and uptime for AI/ML platform infrastructure and services. 

Collaborate with product, security, and compliance teams to ensure platform alignment with enterprise policies. 

Enterprise MLOps / AIOps Enablement 

Design and implement standardized MLOps and AIOps patterns across business units, enabling consistent and scalable practices. 

Create and maintain reusable workflows for model development, deployment, and lifecycle management. 

Provide onboarding, enablement, and ongoing support to AI/ML teams adopting enterprise platforms and toolsets. 

DevSecOps Integration 

Embed security into every phase of the ML lifecycle, integrating tools for scanning, policy enforcement, and vulnerability management. 

Implement guardrails, access controls, and automated compliance checks across all CI/CD and IaC processes. 

Ensure platform and model deployments meet enterprise and regulatory requirements. 

Infrastructure as Code & Pipeline Automation 

Develop and maintain IaC templates using tools like Terraform, CloudFormation, and Ansible to provision AI/ML infrastructure. 

Build and optimize CI/CD pipelines for AI/ML assets, including data pipelines, model training workflows, deployment artifacts, and monitoring systems. 

Promote and enforce best practices around automation, observability, and reusability of infrastructure and workflow components. 

Monitoring, Logging, and Operational Visibility 

Implement comprehensive observability for AI/ML workloads using tools like Prometheus, Grafana, Stackdriver, or Datadog. 

Develop alerts and diagnostics for system health, model drift, data integrity, and deployment anomalies. 

Define KPIs and metrics for evaluating operational health and platform usage. 

Education 

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field. 

Experience 

5+ years of experience in MLOps, DevOps, Platform Engineering, or Infrastructure Engineering roles. 

2+ years of experience implementing DevSecOps practices, including secure CI/CD pipelines and policy enforcement. 

Hands-on experience configuring and operating enterprise AI platforms such as IBM watsonx and Google Cloud Vertex AI. 

Proven track record of building and maintaining enterprise-wide ML infrastructure, automation pipelines, and platform support models. 

Technical Skills 

Proficient in IaC tools (Terraform, CloudFormation, Pulumi, Ansible). 

Strong scripting skills in Python, Bash, or similar languages. 

Deep understanding of containerization and orchestration (Docker, Kubernetes). 

Experience with model lifecycle tooling (MLflow, TFX, Vertex AI Pipelines, etc.). 

Familiarity with secrets management, policy-as-code, access control, and monitoring tools. 

Working knowledge of data engineering concepts and integration with model training pipelines. 

Preferred Qualifications 

Cloud certifications (e.g., GCP Professional ML Engineer, AWS DevOps Engineer, IBM Cloud AI Engineer). 

Experience supporting platforms in regulated environments (e.g., HIPAA, FedRAMP, SOX). 

Contributions to open-source MLOps or infrastructure automation projects. 

Familiarity with model governance, monitoring for fairness/explainability, and responsible AI practices. 

Employee Type:
 

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