GCP Infrastructure Engineer - Google Cloud, Terraform, Python, Bash, GKE, CI/CD

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.

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.

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.

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.

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

8+ years of experience in cloud infrastructure engineering, DevOps, or platform engineering.

with GenAI use cases (chatbots, content generation, code assistants, etc.).

Strong hands-on expertise with Google Cloud Platform (GCP), especially Vertex AI.

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.

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.

Experience in MLOps practices for model deployment, monitoring, and retraining.

Exposure to multi-cloud or hybrid cloud environments (GCP, AWS, Azure, on-prem).

Hands-on experience with feature stores (Vertex AI Feature Store, Feast) and ML observability tools (EvidentlyAI, Fiddler).

Knowledge of distributed training frameworks (Horovod, DeepSpeed, PyTorch Distributed).

Contributions to open-source projects in infrastructure, MLOps, or GenAI.

managing infrastructure in regulated industries.

Certified Kubernetes Administrator (CKA) or Certified Kubernetes Application Developer (CKAD)

IBM Certified Watsonx Generative AI Engineer – Associate

IBM Certified Solution Architect - Cloud Pak for Data

Other relevant certifications in AI, Machine Learning, or Cloud-Native technologies.

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