> Markdown version of [/jobs/ext/3754421-infrastructure-engineer-customer-delivery](https://www.wearedevelopers.com/jobs/ext/3754421-infrastructure-engineer-customer-delivery). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Infrastructure Engineer, Customer Delivery

- **Company:** Distyl AI
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $200,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Microsoft Azure, Cloud Engineering, Continuous Integration, Data Integration, Disaster Recovery, Identity and Access Management, Python (Programming Language), Routing, Network Segmentation, Reliability Engineering, Runbook, Software Deployment, Google Cloud, Software Troubleshooting, Amazon Virtual Private Cloud (VPC), AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Data Management, Docker
- **Published:** October 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=336c69e2b793b6a4

## About the Role

* 7+ years of experience in infrastructure engineering, cloud engineering, SRE, or related technical delivery roles
 * Deep production experience in AWS, Azure, or GCP, plus working experience in a second cloud
 * Customer-facing engineering experience with direct responsibility for technical delivery and stakeholder communication. You build trust, clarify requirements, negotiate scope, and keep stakeholders aligned
 * Hands-on experience with networking, IAM, Docker, Kubernetes, and observability, including workload identity, scoped credentials, and resource limits
 * Proven experience writing and maintaining production infrastructure as code
 * Strong production Python or Go experience building reusable infrastructure software, such as provisioning services, controllers, or deployment APIs
 * Experience operating hosted infrastructure with tenant isolation, provisioning, capacity controls, and usage/cost visibility
 * Experience turning customer requirements into production architectures, with clear control-plane/data-plane boundaries, CI/CD, and identity, network, and data integrations
 * Experience delivering under strict access, network, security, and change controls, including private registries or restricted egress
 * Experience implementing network segmentation, data-residency controls, audit trails, SLOs, disaster recovery, and incident response
 * Strong troubleshooting and automation skills. You balance speed with reliability, handle upgrades and stateful changes carefully, and verify AI-assisted work
 * Technical leadership. You lead complex deployments independently. Experience setting technical direction and leading adoption of reusable systems and standards across teams is especially valued
 * Strongly preferred: Building or scaling infrastructure delivery and operations across enterprise customer environments, including production deployments across multiple clouds

 * Preferred: On-premises installs/upgrades, offline operation, or replacing cloud-managed dependencies
 * Preferred: Agent sandboxing or technical AI governance, including model access, routing, data-handling, and audit controls

## Description

We're hiring a hands-on Infrastructure Engineer to lead cloud architecture and delivery of Distyl's AI platform across Distyl-hosted, customer VPC, and on-premises environments. You'll partner with customers, platform engineering, and forward-deployed AI engineers from discovery through production. Scope will reflect your experience, from leading complex deployments to setting technical direction across teams.

You'll translate complex customer architectures and requests into working infrastructure, building reusable patterns and automation that make each launch easier.

You'll own customer deployments end to end and help shape a growing delivery team's practices. Success means faster launches, reliable operations, and less repeated work as deployments scale., * Architecture ownership. Own portable reference architectures across Distyl-hosted, customer VPC, and on-premises deployments, with clear control-plane/data-plane boundaries. Distinguish necessary customer adaptations from avoidable variation across AWS, Azure, and GCP. Integrate identity, networks, data, and model providers with appropriate access, routing, and audit controls

 * Platform and on-premises delivery. Work with forward-deployed AI engineers to deploy and upgrade Distyl's platform using infrastructure as code, Docker, Kubernetes, and shared CI/CD tooling. Support private registries, restricted egress, offline installs/upgrades, and alternatives to cloud-managed dependencies. Validate compatibility and recovery within customer change controls
 * Automation mindset. Turn recurring delivery friction into shared automation. Build provisioning services, controllers, and reusable deployment APIs in Python or Go to make future deployments faster and more reliable
 * Customer relationships and communication. Build trust, clarify requests, explain tradeoffs, and own commitments through production acceptance. Keep customers and developers aligned on readiness, upcoming changes, dependencies, and blockers; escalate risks early
 * Production security and reliability. Implement network segmentation, data-residency controls, and audit trails with security and infrastructure partners. Establish SLOs, observability, actionable alerts, and tested disaster recovery. Own infrastructure incident resolution in assigned environments, coordinating with relevant engineering owners. Use incident and deployment data to improve reliability, runbooks, and support handoffs
 * Hosted infrastructure. Build and operate tenant isolation, provisioning, quotas, autoscaling, metering, and cost attribution for Distyl-hosted deployments, in partnership with platform engineering
 * AI runtime infrastructure. Partner with Runtime on agent sandboxing, workload identity, scoped credentials, network controls, and resource limits across supported deployments
 * AI-assisted engineering. Build agent-assisted workflows for environment checks, troubleshooting, and infrastructure changes. Scope access, verify outputs, and measure improvements in delivery effort and reliability
 * Technical leadership. Set delivery standards, mentor engineers, and help build the delivery team's technical practices. Partner with forward-deployed AI and platform engineers to unblock delivery and turn customer lessons into product improvements