Job Description

JUTEQ is an AI-native and cloud-native technology consulting firm helping enterprises in financial services, telecom, and healthcare build intelligent, production-grade systems. We combine the power of GenAI, cloud architecture, and automation to deliver next-generation business tools.

We're seeking a

DevOps/Site Reliability Engineer (SRE)

with experience in

Google Cloud Platform (GCP)

to lead and evolve our AI infrastructure as we scale multi-tenant agentic systems across automotive and enterprise use cases. This is a hands-on role working at the intersection of automation, observability, and production AI system reliability.

What You'll Work On

Platform Reliability & Automation

Own deployment pipelines, autoscaling, and high-availability for AI microservices running on GCP (Cloud Run, GKE, App Engine) Design and optimize CI/CD pipelines using Cloud Build, Skaffold, GitHub Actions Implement intelligent autoscaling strategies based on LLM cost, latency, and throughput Use Infrastructure as Code (Terraform, Deployment Manager) for repeatable cloud provisioning

Monitoring & Observability

Deploy monitoring and alerting across Cloud Logging, Cloud Monitoring, and custom dashboards for agent performance metrics Define SLOs and SLIs for key services; implement failover and rollback strategies Build observability into agent workflows: latency, success rate, AI token consumption, prompt drift, etc.

Data & AI Infrastructure

Manage access, scaling, and resilience of data services: BigQuery, Firestore, Memorystore, Cloud Storage, Pub/Sub Support model integration workflows with Vertex AI and third-party LLM providers (OpenAI, Anthropic, etc.) Monitor and secure retrieval pipelines (RAG, embedding generation, vector DBs)

Security & Compliance

Implement and maintain IAM policies, workload identity, and service-to-service authentication Lead incident response and postmortem analysis for production outages Ensure systems comply with data residency, privacy, and SOC2/GDPR requirements

What We're Looking For

Experience & Skills

4+ years of DevOps or SRE experience, with at least 2+ years on GCP Strong understanding of GCP products including Cloud Run, GKE, Cloud Build, BigQuery, Pub/Sub, Cloud Monitoring Experience with CI/CD and GitOps workflows (GitHub Actions, ArgoCD, etc.) and Observability/Monitoring Deep knowledge of containerization, Docker, and Kubernetes Familiarity with AI infrastructure (LLMs, prompt evaluation, LangChain/CrewAI patterns) is a strong plus Experience with alerting and logging using Prometheus, Grafana, or GCP-native tools Proficient in scripting (Python, Bash, Go preferred)

Bonus Points

Experience managing infrastructure for AI agent systems or GenAI workloads Familiarity with multi-tenant SaaS platforms Understanding of RAG pipelines, embedding generation, or agent orchestration Certifications: Google Professional Cloud DevOps Engineer or equivalent

Why Join Us

Shape the infrastructure behind real-world AI agents used by automotive dealerships and enterprises Work alongside AI developers, product engineers, and solution architects Ship fast in a zero-to-one environment while building for scale Own platform-level impact across reliability, security, cost, and developer productivity

How to Apply

Please send:

Your resume highlighting DevOps/SRE experience on GCP GitHub or portfolio links showcasing infrastructure projects or CI/CD pipelines (Optional) A short Loom or video describing your favorite system you've built or scaled
Job Type: Full-time