SRE Architect: Reliability & AI-Driven Platform Leader

Cyberobotix LLC

Georgia

On-site

USD 180,000 - 240,000

Full time

3 days ago

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Job summary

Cyberobotix LLC is seeking an SRE Architect to lead reliability strategy and design across critical services on AWS. You will establish standards, blueprints, and frameworks that enable development teams to build and operate resilient systems, while guiding observability maturity and AI-driven reliability initiatives.

As a technical leader, you will mentor engineers, drive SLIs/SLOs, and oversee architectural reviews, production readiness, and toil reduction.

Qualifications

Proven experience in an architectural role, designing solutions for reliability, scalability, and performance.
Deep understanding and practical application of SRE principles (SLIs/SLOs, error budgets, toil reduction, automation, incident management, postmortems).
Experience designing and implementing AIOps, AI-powered observability, or intelligent automation solutions to improve incident detection, root cause analysis, operational efficiency, and service reliability.
Working knowledge of Generative AI, AI agents, MCP, RAG architectures, and enterprise AI platforms, with experience building or operationalizing AI-enabled engineering tools in production environments.
Expertise in cloud computing platforms (e.g., AWS) including infrastructure, networking, and security services
Strong experience with containerization and orchestration technologies (Kubernetes, Docker, serverless computing)
Solid experience designing and implementing observability solutions (e.g., Dynatrace, Prometheus, Grafana, ELK/EFK Stack, Jaeger, OpenTelemetry)
Strong programming/scripting skills (e.g., Python, Go, Bash) for automation and tool development
Excellent analytical, problem-solving, and strategic thinking skills.
Strong communication, collaboration, and leadership skills with the ability to influence technical direction across teams

Responsibilities

Reliability Strategy & Design: Architect and design highly available, scalable, secure, and cost-effective infrastructure and application patterns on AWS.
Define and evangelize SRE best practices, standards, and blueprints for service design, deployment, monitoring, and operational readiness across the engineering organization.
Review current observability implementation to identify gaps and define steps to reach next level maturity of observability setup.
Lead definition and implementation strategy for SLIs, SLOs, and Error Budgets for critical services.
Platform Architecture & Automation: Design solutions to reduce operational toil through automation and improved system design.
Evaluate current SRE tools and automation frameworks and suggest enhancements to improve capability.
Evaluate, prototype, and recommend new technologies, tools, and methodologies to enhance reliability, developer productivity, and operational efficiency.
Technical Leadership & Consultation: Act as a senior technical advisor on reliability, scalability, and performance for development and platform teams.
Provide architectural guidance during the design phase of new services and features to ensure reliability principles are embedded early.
Mentor and coach other SREs and engineers, fostering technical excellence and adherence to SRE principles.
Lead architectural reviews and production readiness assessments for critical systems.
Resilience: Lead blameless postmortems and drive architectural improvements.
Architect and advocate resilience patterns within applications and infrastructure.
Define and drive the organization's AIOps strategy using AI/ML and Generative AI capabilities.
Design intelligent operational platforms with AI agents and telemetry analytics for proactive issue detection and remediation.
Architect AI-powered production intelligence solutions that correlate logs, metrics, traces, and deployment events for insights.
Establish AI governance for production operations including explainability, security, and human-in-the-loop controls.
Collaborate with Data/Platform/Application teams to develop AI-enabled reliability use cases.
Establish mechanisms to continually improve AI model effectiveness and reliability of AI-driven decisions.