Top Engineering Jobs in Atlanta, GA

Own end-to-end verification and validation for complex cloud-native software systems. Design comprehensive testing strategies, build scalable backend and frontend automation frameworks, identify coverage gaps, perform root cause analysis, integrate tests into CI/CD pipelines, and improve testing efficiency. Apply AI-assisted tools to testing and debugging, evaluate emerging AI capabilities, mentor engineers, and raise quality standards across teams.

Leads technical direction for large-scale accessibility systems that detect, remediate, and prevent web accessibility issues. Designs, builds, and operates secure, scalable distributed systems; drives technical roadmaps; mentors engineers; partners with Product and Design; and promotes responsible AI-assisted development. The role requires hands-on software engineering across JavaScript/TypeScript, React, Node.js, databases, storage, real-time data, and AI-enabled tooling.

Provides advanced systems engineering for NETGEAR ProAV customers across the North Central U.S. Responsibilities include customer onboarding, technical training, AV-over-IP solution design, field troubleshooting, commissioning, pre-sales support, system demonstrations, automation tool development, documentation, and industry presentations. The role requires expertise in multicast networking, timing protocols, AV technologies, multi-vendor integrations, and complex deployments, with travel of up to 60%.

Provides 24x7 operational support for managed Linux customer environments, including installation, incident resolution, patching, escalation support, documentation, customer communication, and process automation. Collaborates with operations and architecture teams to maintain service availability and resolve high-severity incidents.

Develops, tests, supports, and maintains scalable web applications and software components across front-end and back-end technology stacks. Partners with Product Management to design solutions, contributes to Agile planning and architecture, troubleshoots deployed products, reduces technical debt, and provides technical guidance to less experienced engineers.

Owns production infrastructure across Kubernetes, CI/CD, Linux systems, virtual machines, and AI accelerator environments. Responsibilities include incident response, troubleshooting, automation, monitoring, alerting, patching, upgrades, certificate renewals, capacity checks, runbook development, and on-call support. The engineer collaborates across IT and product software teams and contributes to infrastructure design and build work.

Build and deploy data-driven solutions for government customers, including data pipelines, backend services, analytical tools, entity resolution, risk scoring, and AI-assisted features. Lead engagements from discovery through deployment, integrate external data sources, support sensitive and air-gapped environments, validate findings, and translate customer needs into practical technical solutions while collaborating with Product and R&D.

Build and maintain Python automations and multi-system integrations for 401(k) administration. Responsibilities include developing API and webhook integrations, managing production jobs, troubleshooting errors, refactoring legacy code, deploying projects, improving logging and code quality, documenting processes, and collaborating with business users, vendors, and IT. The role requires ownership of automation projects from discovery through production and focuses on resilient, scalable workflow improvements.

Create and maintain runnable, tested developer-facing documentation and example repos (Terraform/CloudFormation/CDK, SDKs, CLI) for AWS workloads; instrument docs for activation and trial conversion; collaborate with engineering, product, and growth to optimize onboarding and self-serve success.

Owns end-to-end technical architecture across Rails, Python machine-learning services, agent infrastructure, and data platforms. Leads scalability, reliability, cost optimization, model infrastructure, architectural reviews, technical roadmap decisions, incident response, code reviews, and engineering mentorship. The role remains hands-on, requiring production coding and expertise in computer vision, LLM systems, cloud infrastructure, databases, background jobs, Kubernetes, and DevOps practices.