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Salary: USD 200,000 - 250,000 / annual
Distyl AI is an applied AI technology company partnering with major institutions to rearchitect critical operations for the frontier of AI. The company works with the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations, deploying technologies that power AI-native operations affecting hundreds of millions of consumer interactions, tens of millions of supply chain transactions, and millions of patient journeys.
You will be a hands-on Infrastructure Engineer leading 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, with scope reflecting your experience—from leading complex deployments to setting technical direction across teams.
Key responsibilities include:
- Owning portable reference architectures across Distyl-hosted, customer VPC, and on-premises deployments with clear control-plane/data-plane boundaries, distinguishing 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.
- Working 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.
- Turning recurring delivery friction into shared automation by building provisioning services, controllers, and reusable deployment APIs in Python or Go.
- Building trust with customers, clarifying requests, explaining tradeoffs, and owning commitments through production acceptance. Keep customers and developers aligned on readiness, upcoming changes, dependencies, and blockers.
- Implementing network segmentation, data-residency controls, and audit trails. Establish SLOs, observability, actionable alerts, and tested disaster recovery. Own infrastructure incident resolution in assigned environments.
- Building and operating tenant isolation, provisioning, quotas, autoscaling, metering, and cost attribution for Distyl-hosted deployments in partnership with platform engineering.
- Partnering with Runtime on agent sandboxing, workload identity, scoped credentials, network controls, and resource limits across supported deployments.
- Building agent-assisted workflows for environment checks, troubleshooting, and infrastructure changes, with appropriate access scoping and output verification.
- Setting delivery standards, mentoring engineers, and helping 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.
Requirements:
- 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
- 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 (provisioning services, controllers, 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/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; balance speed with reliability and handle upgrades and stateful changes carefully
- Technical leadership: 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
