Senior MLOps

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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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