MLops Engineer

Role & responsibilities

Required Qualifications

Education

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

Experience

• 5+ years in MLOps, DevOps, Platform Engineering, or Infrastructure Engineering.
• 2+ years applying DevSecOps practices (secure CI/CD, vulnerability management, policy enforcement).
• Hands-on experience configuring and managing enterprise AI/ML platforms (IBM watsonx, Google Vertex AI).
• Demonstrated success in building and scaling ML infrastructure, automation pipelines, and platform support models.

Technical Skills

• Proficiency with IaC tools (Terraform, Pulumi, Ansible, CloudFormation).
• Strong scripting skills in Python and Bash.
• Deep understanding of containerization and orchestration (Docker, Kubernetes).
• Experience with model lifecycle tools (MLflow, TFX, Vertex Pipelines, or equivalents).
• Familiarity with secrets management, policy-as-code, access control, and monitoring tools.
• Working knowledge of data engineering concepts and their integration into ML pipelines.

Preferred

• Cloud certifications (e.g., GCP Professional ML Engineer, AWS DevOps Engineer, IBM Cloud AI Engineer).
• Experience supporting platforms in regulated industries (HIPAA, FedRAMP, SOX, PCI-DSS).
• Contributions to open-source projects in MLOps, automation, or DevSecOps.
• Familiarity with responsible AI practices including governance, fairness, interpretability, and explainability.
• Hands-on experience with enterprise feature stores, model monitoring frameworks, and fairness toolkits.

Job Summary

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.

Key Responsibilities

Platform Engineering & Operations

• Lead the provisioning, configuration, and ongoing support of IBM watsonx and Google Cloud Vertex AI platforms.• Ensure platforms are production-ready, secure, cost-efficient, and performant across training, inference, and orchestration workflows.
• Manage lifecycle tasks such as patching, upgrades, integrations, and service reliability.
• Partner with security, compliance, and product teams to align platforms with enterprise and regulatory standards.

Enterprise MLOps / AIOps Enablement

• Define and implement standardized MLOps/AIOps practices across business units for consistency and scalability.
• Build and maintain reusable workflows for model development, deployment, retraining, and monitoring.
• Provide onboarding, enablement, and support to AI/ML teams adopting enterprise platforms and tools.
• Support development/deployment of GenAI applications and maintain them at an Enterprise scale.

DevSecOps Integration

• Embed security and compliance guardrails across the ML lifecycle, including CI/CD pipelines and IaC templates.
• Implement policy-as-code, access controls, vulnerability scanning, and automated compliance checks.
• Ensure all deployments meet enterprise and regulatory requirements (HIPAA, SOX, FedRAMP, etc.).

Infrastructure as Code & Automation

• Design and maintain IaC templates (Terraform, Pulumi, Ansible, CloudFormation) for reproducible ML infrastructure.
• Build and optimize CI/CD pipelines for AI/ML assets including data pipelines, training workflows, deployment artifacts, and monitoring systems.
• Enforce best practices around automation, reusability, and observability of infrastructure and workflows.

Monitoring, Logging & Observability

• Implement comprehensive observability for AI/ML workloads using Prometheus, Grafana, Stackdriver, or Datadog.
• Monitor both infrastructure health (CPU, memory, cost) and ML-specific metrics (model drift, data integrity, anomaly detection).
• Define KPIs and usage metrics to measure platform performance, adoption, and operational health.