Cloud Engineer

AI Cloud Engineer — Production AI Infrastructure
Location: Turkey — Ankara preferred; qualified candidates from other locations are welcome to apply
Work arrangement: Fully remote contract role
Primary cloud: Google Cloud Platform; AWS experience strongly preferred
About Us
We are a stealth AI startup in San Francisco and London built around significant proprietary intellectual property and a clear ambition: to help define what comes next in artificial intelligence.
We believe the next foundational challenge in AI is enabling systems to continue learning after deployment—to adapt to new information, experience, and feedback during runtime while preserving and building on what they already know.
Our work in adaptive continual learning directly addresses this challenge. We believe the technology we have developed can help shape the next chapter of AI and change how intelligent systems learn, operate, and evolve in the real world.
We are looking for an experienced cloud engineer who can build the secure, scalable infrastructure required to bring this technology into production.
The Role
We are seeking an AI Cloud Engineer with deep experience in Google Cloud and production-grade distributed systems. Strong AWS experience is also highly valued.
You will design, build, and operate the cloud platform behind our AI systems. This includes model training and inference infrastructure, agent and MCP services, secure tenant isolation, automated deployment, observability, scaling, and disaster recovery.
You will work across cloud architecture, platform engineering, security, and AI operations. This is a hands-on role with substantial ownership and direct influence over the architecture and engineering standards of a new company.
What You Will Do
Design and operate secure, production-grade AI infrastructure on Google Cloud.
Deploy and manage model training, inference, agent, and continual-learning workloads.
Build a multi-tenant platform with strong isolation across customer data, compute, storage, networking, identities, and secrets.
Define clear control-plane and data-plane boundaries for tenant-aware services.
Deploy and operate MCP servers, agent gateways, model endpoints, and supporting backend services.
Build scalable infrastructure using services such as GKE, Cloud Run, Vertex AI, Pub/Sub, Cloud Storage, and managed databases.
Implement workload-based autoscaling for APIs, agents, background jobs, inference services, and GPU workloads.
Design systems for high availability, fault tolerance, regional recovery, queue management, backpressure, and graceful failure.
Establish infrastructure as code and repeatable environments using tools such as Terraform and Helm.
Build secure CI/CD pipelines for application, infrastructure, and model deployments.
Implement identity and access controls, workload identity, network policies, encryption, secret management, audit logging, and least-privilege access.
Create end-to-end observability using metrics, logs, traces, dashboards, alerts, and service-level objectives.
Improve cloud performance while controlling infrastructure, storage, networking, and accelerator costs.
Support portability and selected deployments on AWS when required.
Document architecture and communicate operational risks, tradeoffs, and technical decisions clearly.
What You Bring
Significant experience designing and operating production systems on Google Cloud.
Strong knowledge of Kubernetes, containers, networking, load balancing, identity management, and distributed systems.
Practical experience with GKE and related Google Cloud services.
Demonstrated experience designing secure multi-tenant SaaS or AI platforms.
A clear understanding of tenant isolation across application, data, infrastructure, and security boundaries.
Experience deploying AI or machine-learning workloads, including model serving, training pipelines, GPU infrastructure, or agent systems.
Strong infrastructure-as-code experience, preferably with Terraform.
Experience building CI/CD pipelines and reliable deployment strategies such as rolling, blue-green, or canary releases.
Strong knowledge of observability, incident response, capacity planning, backup, and disaster recovery.
Proficiency in Python, Go, or another language used for platform automation and backend services.
Strong written and spoken English, with the ability to explain complex architecture clearly.
Initiative, accountability, and the ability to work independently while collaborating closely with AI and product engineers.
Especially Valuable Experience
Production experience with AWS services such as EKS, ECS, SageMaker, S3, IAM, and managed databases.
Operating GPU-based training or inference workloads at scale.
Building infrastructure for MCP servers, AI agents, agent gateways, or model-routing systems.
Kubernetes security, workload identity, service meshes, policy enforcement, and secrets management.
Multi-region architectures, tenant-aware metering, quotas, rate limits, and usage-based billing.
Experience with vector databases, event-driven systems, streaming pipelines, and distributed storage.
FinOps experience controlling the cost of compute-intensive AI workloads.
Why Join Us
You will join at a formative stage and build the production foundation for technology designed to address one of AI’s next defining challenges.
Your work will determine how our systems scale, how securely customer workloads are isolated, and how reliably our technology reaches production. You will have meaningful authority over major architectural decisions and the opportunity to build the platform with clarity and discipline from the beginning.
If you want to build the cloud foundation for a new generation of adaptive AI systems, we would like to hear from you.

Work arrangement
Yes