Senior Systems & Infrastructure Engineer

buzzsolutions; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Buzz Solutions seeks an experienced Systems & MLOps engineer to own cloud infrastructure for its AI-powered PowerAI platform. You will partner with backend, ML and platform teams to run secure, observable and scalable systems on Google Cloud, AWS and Azure while ensuring cost efficiency and reliability across high-volume image processing pipelines.

The role emphasizes building infrastructure as code, designing scalable clusters and enabling GitOps workflows with a focus on security and

Qualifications

• 6+ years building and operating production systems.
• Hands-on cloud exposure across at least one major provider (GCP preferred).
• Production Kubernetes experience with cluster design, upgrades and scaling.
• Terraform at scale with modular design and environment parity.
• Postgres and Redis infrastructure with replication, backups and capacity planning.
• CI/CD and container delivery with Docker and a GitOps or equivalent release process.
• Observability: set SLOs and reliable alerting; on-call readiness.
• Security fundamentals: least privilege, secrets management and network segmentation.

Responsibilities

• Own cloud infrastructure and ensure security, observability and cost-efficiency for Buzz Solutions platform.
• Run cloud, clusters, data stores and observability stack; collaborate with backend engineers on services and pipelines.
• Define provisioning, deployment, scaling and operation of systems; write automation code for infra.

Skills

Production systems

Cloud infrastructure

Kubernetes

Terraform

Postgres/Redis

CI/CD

Observability

Security fundamentals

Tools

GCP

AWS

Azure

PostgreSQL

Redis

Docker

Kubernetes

Argo CD

Terraform

Job description

Job Description

Buzz Solutions uses AI to keep the power grid safe and reliable. Much of the world's power grid is decades old, and utilities must inspect it every year to catch faults before they cause outages or wildfires. Those inspections produce millions of drone, helicopter and fixed-wing images a year, and most are still reviewed by hand. Our PowerAI platform changes that: machine-vision models find damaged insulators, conductors, hardware, poles and towers, flag vegetation encroachment, and help utility teams decide what to fix first. The work matters in a direct way. Every fault we help a utility find sooner is a lower chance of a blackout or a fire. You'll join a small team where engineers own real systems, see their work in customers' hands quickly, and help shape how a critical industry adopts AI.

The Systems & MLOps team runs the foundation that every Buzz product stands on. Our ML team builds the models and our Platform team ships PowerAI features; we make sure the infrastructure, data pipelines and model-serving layer underneath them are secure, observable and ready to scale. A single inspection can mean hundreds of thousands of high-resolution images moving through upload, preprocessing, AI inference and into the records utilities act on. We own the services and event pipelines that carry that data, on Google Cloud, PostgreSQL, Pub/Sub and Kubernetes. This matters because a lost or duplicated image is a structure nobody reviews. When data moves reliably and fast, customers get results sooner and can trust that every asset was inspected.

You'll own the cloud infrastructure that every Buzz product runs on, and make it secure, observable, cost-efficient and ready for 10x more customers, imagery and inference traffic. This is an infrastructure and DevOps role that works closely with our backend engineers: you run the cloud, clusters, data stores and observability platform, and you write code and help the backend team build the services, schemas and pipelines that run on them.

You'll set how we provision, deploy, scale and operate our systems, and write much of the code that does it. You'll work closely with the Systems, ML and Platform leads, and with the solutions team that deploys PowerAI into utility environments with strict security and compliance requirements.

What you'll own

• Cloud environments. Google Cloud, with AWS and Azure footprints where customers require them: networking, IAM, compute, storage and managed databases.
• Infrastructure as code. Terraform for every environment, with reviewed, repeatable deployments and parity across dev, staging and production.
• Cluster architecture, upgrades, autoscaling, GPU node pools for inference, ingress, secrets and workload isolation.
• Data store infrastructure. Fast, reliable and fault-tolerant Postgres/PostGIS, Redis, object storage and queues.
• Autoscaling, capacity planning and back-pressure so bursty inspection workloads scale out without dropping work.
• CI/CD. Build, test, security scanning, container image management and progressive deployment with GitOps (Argo CD or similar).
• Metrics, logs and traces with SLOs, alerting and dashboards the whole team uses.
• Backup and recovery. Backup, disaster recovery and business continuity, with tested recovery objectives.
• Security and compliance. Least-privilege access, network segmentation, encryption and secrets management, plus support for SOC 2, customer security reviews and single-tenant deployments.
• Cloud cost. Tagging, budgets, rightsizing, committed-use planning and per-customer unit economics.
• Turn loosely defined infrastructure problems into scoped designs, set standards and review code.
• Incident response, on-call design and blameless postmortems, with measurable gains in uptime and recovery time.

What you'll bring

We don't expect anyone to match every line. If you meet most of the must-haves and are excited about the work, please apply. Equivalent tools count: if you've done this work with a different queue, cloud or framework, tell us about it.

Must-haves

• 6+ years building and operating production systems, with at least 3 years owning cloud infrastructure end to end.
• Deep hands-on experience with at least one major cloud (GCP preferred, AWS or Azure) across networking, IAM, compute, managed databases and object storage.
• Production Kubernetes: you have designed, upgraded, debugged and scaled clusters, not only deployed to them.
• Terraform at scale: module design, state management, review workflows and environment parity.
• Postgres and Redis infrastructure: replication, high availability, backups, connection pooling and capacity planning.
• CI/CD and container delivery with Docker, security scanning and a GitOps or equivalent release process.
• Observability and on-call: you have set SLOs, built alerting that people trust and led incidents to resolution.
• Security fundamentals in practice: least privilege, secrets management, network segmentation, encryption and vulnerability