DevOps and Cloud Infrastructure Engineer

About Turing

Turing is a leading AI company accelerating the advancement and deployment of frontier AI systems. We work with the world’s top AI labs and enterprises to improve capabilities in reasoning, coding, agentic behavior, multimodality, and other advanced AI domains.

Role Overview

We are looking for a hands-on DevOps and Cloud Infrastructure Engineer to build and operate the infrastructure behind Lazarus, a large-scale platform for PII detection, redaction, and human review.

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.

What You’ll Do

• 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.

What We’re Looking For

• 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
• Working experience with AWS services, particularly Amazon S3 and cross-cloud data movement.
• Strong Linux administration and shell-scripting skills.
• Strong Python skills for infrastructure automation, operational tooling, and data-processing workflows.
• Strong Docker and containerization experience.
• Experience designing and operating large-scale batch or data-processing pipelines.
• Solid understanding of queues, worker parallelism, retries, checkpoints, idempotency, timeouts, and failure recovery.
• Experience securely processing and transferring sensitive or regulated data.
• Experience with CI/CD tools and production deployment workflows.
• Experience debugging CPU, memory, disk I/O, network, storage, API, and GPU performance issues.
• Exposure to ML infrastructure, GPU workloads, OCR, NLP, LLM serving, or model inference systems.
• Strong problem-solving skills and the ability to independently investigate and resolve production issues.

Perks of Freelancing With Turing

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

Offer Details

• Commitments Required:  40 hours per week with overlap of 6 hours per day with PST. 
• Duration of Contract: 1 month (adjustable based on engagement)