GCP DevOps Engineer

Key Responsibilities:

Cloud Infrastructure & Platform Engineering

• Design, provision, and maintain scalable, secure, and cost-efficient infrastructure for GenAI applications on GCP.
• Deploy and manage containerized workloads using Docker and Kubernetes (GKE).
• Configure and optimize Vertex AI and IBM Watsonx platforms for training, fine-tuning, and serving LLMs and other generative models.
• Implement high-performance GPU/TPU clusters to support distributed training and large-scale inference.
• Ensure business continuity through backup, disaster recovery, and multi-region deployments.

Automation & Reliability

• Develop and maintain Infrastructure as Code (IaC) templates with Terraform, or Cloud Deployment Manager.
• Adopt GitOps practices (Flux) for infrastructure lifecycle management.
• Build and optimize CI/CD pipelines for data pipelines, model workflows, and GenAI applications.
• Apply SRE principles (SLIs, SLOs, SLAs) to guarantee platform reliability and uptime.

Security, Governance & Compliance

• Embed DevSecOps best practices across the infrastructure lifecycle, including policy-as-code, vulnerability scanning, and secrets management.
• Enforce identity and access management (IAM), network segmentation, and data encryption in compliance with standards (HIPAA, SOX, GDPR, FedRAMP).
• Collaborate with enterprise security and compliance teams to implement governance frameworks for GenAI platforms.

Monitoring, Observability & Cost Optimization

• Implement observability stacks (Prometheus, Grafana, Cloud Monitoring, Datadog) for both infra health and ML-specific metrics (model drift, data anomalies).
• Define KPIs to monitor system health, performance, and adoption across AI workloads.
• Optimize cloud cost efficiency for GPU/TPU-intensive workloads using autoscaling, preemptible instances, and utilization monitoring.

Collaboration & Enablement

• Partner with data scientists, ML engineers, and software teams to streamline GenAI application development and deployment.
• Provide onboarding, documentation, and reusable templates to enable faster adoption of AI infrastructure.
• Stay current with the latest advancements in GenAI, cloud-native infrastructure, and container orchestration.

Required Education

Bachelors or master’s degree in computer science, Software Engineering, or a related field.

Required Experience

• 5+ years

  of experience in cloud infrastructure engineering,

  DevOps,

  or platform engineering.
• Experience with GenAI use cases (chatbots, content generation, code assistants, etc.).
• Strong hands-on expertise with

  Google Cloud Platform (GCP),

  especially

  Vertex

  AI.

• Experience with

  IBM Watsonx for AI application

  deployment and management.
• Proven skills in

  Docker, Kubernetes (GKE),

  and container orchestration at scale.
• Proficiency in

  Python, Bash,

  or other relevant scripting languages.
• Strong understanding of cloud networking, IAM, and security best practices.
• Experience with CI/CD tools (GitHub Actions, GitLab CI, Jenkins) and IaC tools (Terraform, Pulumi, Ansible, Deployment Manager).
• Familiarity with data pipelines and integration tools (Dataflow, Apache Beam, Pub/Sub, Kafka).
• Excellent problem-solving, debugging, and communication skills.

Preferred Experience

• Experience in MLOps practices for model deployment, monitoring, and retraining.
• Exposure to multi-cloud or hybrid cloud environments (GCP, AWS, Azure, on-prem).