Senior Engineer, Cloud Infrastructure (US)

Code and Theory is seeking a senior cloud engineer who wants to own the full technical lifecycle of enterprise client deployments — from assessing a client's existing infrastructure and designing the integration architecture, to provisioning the environment and keeping it running in production. This is a hands-on role that also requires real client engagement: you will sit in technical working sessions, work directly with client DevOps teams, and be the person who translates security and compliance requirements into concrete infrastructure decisions.

You will be working on a platform that connects our AI product to enterprise marketing environments across different cloud providers. Each client brings its own infrastructure, data stack, and security perimeter. Your job is to figure out how to connect to it, build the integration reliably, and hand it off in a state that can be operated and maintained over time.

WHAT YOU'LL DO

Assess each client's cloud infrastructure, data stack, and security perimeter before any build starts — translate findings into a concrete integration plan

Design and provision client environments using Terraform — networking, IAM, container orchestration, managed storage, and secrets management across GCP, AWS, or Azure depending on the client

Deploy and operate LLM inference pods in client cloud environments — managing API integration, rate limits, latency, and failure handling without needing a data scientist in the room

Build and maintain integration layers that connect client data sources to the AI layer — you own the plumbing that makes inference useful; the data science team owns what runs on top of it

Deploy and maintain containerized workloads via Helm — orchestration, ETL workers, and AI inference pods running inside the client's cloud perimeter

Own data pipeline deployments end to end — scheduling, pagination, retry logic, and rate-limit management against client API gateways

Manage distributed ETL jobs at scale — JSON flattening, schema enforcement, and structured output delivery

Enforce data residency requirements — raw data stays inside the client's environment, only structured output leaves to our shared infrastructure

Serve as the primary technical contact for each client's DevOps and infrastructure teams throughout the engagement

Lead technical working sessions with client teams — validate configurations, confirm IAM and credential models, review cluster specs before deployment

Triage and resolve pipeline and infrastructure failures across multiple client environments simultaneously

Implement container security standards — non-root execution, read-only filesystems, startup integrity hashes, tamper protection

Mentor junior and mid-level engineers and contribute implementation patterns to the shared playbook after every client engagement

WHAT YOU'LL NEED

6+ years of cloud engineering experience — production experience on at least two of GCP, AWS, or Azure, and willing to operate across all three depending on client environment

Deep Terraform experience — you have provisioned multi-environment, multi-tenant production infrastructure from scratch, not just applied existing configurations

Comfortable with Kubernetes and Helm in production — deploying, debugging, scaling, and securing containerized workloads, not just running them

Have run data pipelines in production — you know what breaks, how to recover, and how to build retry and backoff logic that actually holds up

Hands-on experience with distributed processing at scale — not just theoretical knowledge

Enterprise API integration experience — OAuth 2.0, API key management, rate limiting, API gateways — end to end

Solid security fundamentals — container hardening, credential management, and data residency enforcement in environments where it actually matters

Have worked directly with client or customer engineering teams — comfortable leading technical conversations with enterprise DevOps and infrastructure teams, not just supporting them

You write things down — runbooks, integration notes, playbook contributions. The next engineer should be able to operate what you built

Hands-on experience integrating LLM APIs (Anthropic, OpenAI, or equivalent) into production pipelines — not as an end user, but wiring inference into systems that run at scale across real client environments

NICE TO HAVE

Experience with:

LLM inference infrastructure

Marketing technology stacks (DAM, CDP, CRM)

Multi-tenant client environments

A cloud certification (AWS Solutions Architect, GCP Professional Cloud Architect, or Azure equivalent)

Agency, consultancy, or product company background serving multiple enterprise clients simultaneously

ABOUT US

Born in 2001, Code and Theory is a digital-first creative agency that sits at the center of creativity and technology. We pride ourselves on not only solving consumer and business problems, but also helping to establish new capabilities for our clients. With a global clien…