Remote Python Automation Engineer Jobs in Joliet, IL

No H1 or C2C. Must be Permanent Resident or US Citizen

Senior DevOps Platform Engineer

Description and Requirements

About Our Team

We are building Quantum, a next‑generation hybrid AI platform that spans Windows, Android, and cloud. As part of this vision, we are expanding the engineering organization supporting cross‑device Personal AI.

We are hiring Senior DevOps / Platform Engineers to build and operate the core automation, infrastructure, and service platforms that enable secure, reliable, and high‑velocity delivery of AI systems across device, edge, and cloud.

Depending on your background, you may be aligned to Platform Engineering, Observability, Operations, or Service Reliability—based on experience and organizational need.

Operates with the speed, ownership, and creativity of a startup, supported by the scale, resources, and technical depth. We are building foundational systems from the ground up—intentionally, pragmatically, and with a culture of engineering excellence.

Location: Open to remote work in the US. The preferred work location is Chicago, IL.

What You Might Work On

As a Senior DevOps / Platform Engineer, you may be responsible for a subset of the following areas depending on team placement:

CI/CD, Automation & Tooling

• Designing, implementing, and improving CI/CD pipelines for AI, platform, and application teams.
• Building automation and developer tooling to improve productivity and consistency.
• Developing infrastructure‑as‑code for cloud and hybrid environments (Terraform, Bicep, etc.).

Platform & Infrastructure Engineering

• Implementing scalable, secure, and resilient infrastructure on Azure and Kubernetes.
• Building and operating hybrid systems spanning device, edge, and cloud compute.
• Enabling reliable platform services that support inference, data pipelines, and high‑performance AI workloads.

Observability & Telemetry

• Implementing and enhancing observability systems using OpenTelemetry, Grafana, Prometheus, Loki, and related tooling.
• Ensuring platform telemetry is accurate, actionable, and tied to performance and reliability outcomes.
• Building dashboards and analytics for service health and operational insight.

Deployment & Release Engineering

• Improving deployment workflows, safety, consistency, and traceability.
• Supporting progressive delivery patterns including canaries, staged rollouts, and automated rollbacks.
• Optimizing CI/CD and deployment tooling for hybrid AI services.

Collaboration & Reliability Culture

• Partnering closely with SRE, AI/ML, security, firmware, and product engineering teams.
• Contributing to system design discussions with a focus on automation, scalability, and operational best practices.
• Helping define and evolve platform engineering standards, patterns, and conventions.

Basic Qualifications

• 10+ years in DevOps, Platform Engineering, Cloud Engineering, or related fields
• Bachelor’s Degree in Computer Science, Engineering, or a related technical field
• Strong experience building and operating infrastructure in Azure, AWS, or GCP
• Proficiency with CI/CD systems, build automation, and deployment pipelines
• Experience with Infrastructure as Code (Terraform, ARM/Bicep, CloudFormation, etc.)
• Strong development or scripting skills (Python, Go, Bash, or similar)
• Hands-on experience with Docker and Kubernetes
• Understanding of observability fundamentals (metrics, logs, tracing)

Preferred Qualifications

• Deep experience with Azure cloud architecture and DevOps tooling
• Strong hands‑on work with OpenTelemetry (instrumentation, pipelines)
• Experience with Grafana, Prometheus, Loki, Tempo, or similar observability tools
• Experience supporting AI/ML workloads or GPU‑accelerated compute environments
• Familiarity with event‑driven systems and operationalizing data pipelines
• Experience contributing to or running on‑call rotations
• Passion for automation, developer experience, and infrastructure reliability at scale

What Success Looks Like

• CI/CD pipelines are fast, stable, and trusted.
• Platform infrastructure becomes more automated, observable, and scalable.
• Telemetry and dashboards provide clear visibility into system health.
• Deployments are consistent, safe, and repeatable.
• Engineering teams move faster thanks to strong platform foundations.
• Hybrid AI platform becomes increasingly reliable, efficient, and easy to operate.