Senior MLOps

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About The Role :

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.

Qualifications

Education

- Bachelors or Masters 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.

Permanent

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