You will design secure, scalable, observable, and cost-efficient infrastructure for processing sensitive datasets across text, documents, images, and other file formats.

The role involves supporting CPU- and GPU-intensive workloads, large-scale batch processing, ML inference pipelines, and production cloud operations across GCP and AWS.

Perks of Freelancing With Turing

• Work in a fully remote environment.
• Opportunity to work on cutting-edge AI projects with leading LLM companies.

Duration of Contract: 1 month (adjustable based on engagement).

• Design, provision, and operate GCP infrastructure for PII detection and redaction pipelines.
• Build and manage workloads across Cloud Run, Cloud Run Jobs, Compute Engine, GKE, and GPU-backed infrastructure.
• Design scalable batch-processing systems capable of processing hundreds of thousands of files.
• Implement worker parallelism, queues, retries, checkpointing, idempotency, timeouts, and failure recovery.
• Manage data movement across Google Cloud Storage, Amazon S3, VMs, containers, and external storage systems.
• Secure sensitive datasets using IAM, service accounts, Secret Manager, private networking, controlled egress, IAP, encryption, bucket-level access controls, and audit logging.
• Deploy and operate containerized Python and ML workloads using Docker.
• Support PII and ML services such as Google Sensitive Data Protection, Presidio, OCR and vision systems, NER models, and LLM-based validation pipelines.
• Provision and manage GPU infrastructure, including drivers, CUDA, quotas, autoscaling, and model-serving environments.
• Support model-serving stacks such as vLLM, Hugging Face Transformers, and Triton.
• Build and maintain CI/CD pipelines for Cloud Run, VMs, containers, and related services.
• Establish observability through centralized logging, metrics, alerting, job tracking, worker-health monitoring, and infrastructure utilization dashboards.
• Optimize throughput and cost through infrastructure selection, concurrency tuning, autoscaling, storage access patterns, and API rate-limit management.
• Develop backup, recovery, migration, and disaster-recovery processes for large datasets and cloud resources.
• Troubleshoot production issues involving networking, storage, IAM, containers, APIs, compute, and ML infrastructure.

• 5+ years of experience in DevOps, cloud infrastructure, platform engineering, or a related field.

Strong hands-on experience with GCP, including:

• Cloud Run and Cloud Run Jobs.
• Compute Engine and GKE.
• Google Cloud Storage.
• IAM and service accounts.
• Secret Manager.
• VPC and private networking.
• Identity-Aware Proxy.
• Artifact Registry.
• Cloud Logging and Monitoring.

Work arrangement: Fully remote