Machine Learning Expert

Strictly Looking for 15Days to 30Days Notice period candidates

Depute to Technology Client under the payroll of Randstad

**********Apply if your accepting above terms*************

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.

Qualifications

Education:

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

  .

naresh.randstad@gmail.com