Lead cloud architecture and delivery of Distyl's AI platform across various environments while ensuring reliable operations.
Posted by employer 2 days ago
First seen on Joblaze 19 hours ago
Last verified on the company career page 19 hours ago
What you'll build
Must have
Nice to have
Practical constraints
Requirements
Not disclosed in this posting: visa sponsorship.
Benefits
Joblaze summary
The Infrastructure Engineer at Distyl AI is responsible for leading the cloud architecture and deployment of the company's AI platform across various environments, ensuring seamless integration and reliable operations. Key skills include expertise in AWS, Azure, or GCP, along with hands-on experience in Docker, Kubernetes, and infrastructure as code, particularly in Python or Go. This role is suited for seasoned professionals with a strong background in customer-facing engineering and technical delivery. Distyl AI emphasizes a collaborative culture, making this position ideal for those who thrive in dynamic team environments.
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Quick facts
From the original posting
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
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
The base salary range for this role is $200,000-$250,000, depending on experience, location, and level. In addition to base compensation, this role is eligible for meaningful equity, along with a comprehensive benefits package
Flexible time off
Complimentary in-office lunches and snacks provided
Ownership of high-impact projects across top enterprises
#LI-Hybrid
Standard company text repeated across Distyl AI's postings is omitted here.