Solution Architect AI Platform Reliability & SRE (Mythos SRE)

Job Description

Job Description

Job Title:  Solution Architect – AI Platform Reliability & SRE (Mythos SRE)

Location: Remote (Within USA)

Hire Type: Contract

Pay Range: Competitive Hourly Rate

Work Model: Remote with periodic travel to Buffalo, NY

Schedule: Monday – Friday, Standard Business Hours

Recruiter Contact: Samantha Marranca | View phone number on us.fitly.work | View email address on us.fitly.work

NO C2C, NO sponsorship given at this time

Nature & Scope:

Positional Overview

Our client is seeking an experienced Solution Architect to support the reliability, scalability, observability, and operational excellence of its enterprise AI platform, Mythos. This role serves as the solution architecture extension of Enterprise Architecture and AI Platform teams, translating strategic platform designs into detailed operational architectures that enable highly available, resilient, and scalable AI services.

The Solution Architect will partner closely with Site Reliability Engineering (SRE), Platform Engineering, Infrastructure, Cloud Operations, and Application Development teams to establish architecture patterns and operational frameworks that support enterprise AI workloads across cloud and co-location environments.

This position is ideal for a hands-on architect with expertise in cloud infrastructure, platform engineering, observability, reliability engineering, and large-scale distributed systems.

Role & Responsibility:

Tasks That Will Lead  To Your Success

AI Platform Reliability Architecture

• Translate enterprise AI platform architecture into detailed operational and infrastructure solution designs. 
• Define reliability, scalability, resiliency, and availability architecture standards for AI workloads. 
• Develop architecture patterns supporting highly available and fault-tolerant AI services. 
• Support enterprise AI platform growth through scalable infrastructure and platform design. 
• Establish architecture guidance for production readiness and operational excellence.

Site Reliability Engineering & Operational Excellence

• Define architecture patterns supporting SRE best practices across AI platforms. 
• Support implementation of Service Level Indicators (SLIs), Service Level Objectives (SLOs), and error budget frameworks. 
• Develop operational readiness standards and deployment validation processes. 
• Establish reliability engineering practices that improve system stability and performance. 
• Partner with engineering teams to improve incident prevention, detection, and response capabilities.

Scalability & Performance Optimization

• Design solutions supporting large-scale AI workloads and model-serving environments. 
• Establish architecture patterns that optimize platform performance and resource utilization. 
• Support capacity planning and infrastructure scaling strategies. 
• Identify performance bottlenecks and recommend architectural improvements. 
• Collaborate with engineering teams to improve application and platform efficiency.

Observability & Monitoring

• Design enterprise observability frameworks supporting AI platform operations. 
• Establish telemetry standards providing visibility into system health, model performance, operational metrics, and risk indicators. 
• Define monitoring, alerting, logging, and tracing strategies. 
• Support implementation of observability tools and telemetry platforms. 
• Ensure operational teams have actionable insights supporting platform reliability and performance.

Infrastructure & Automation

• Develop architecture guidance for Infrastructure as Code (IaC) and platform automation. 
• Support CI/CD pipeline architecture and deployment automation strategies. 
• Establish repeatable operational patterns supporting cloud and co-location environments. 
• Promote infrastructure standardization and operational consistency. 
• Collaborate with Platform Engineering teams on automation and operational tooling initiatives.

AI Operational Governance

• Support architecture strategies for AI model monitoring and drift detection. 
• Establish operational frameworks supporting AI governance and platform controls. 
• Define reliability patterns for embedded AI capabilities within enterprise applications. 
• Ensure platform operations align with enterprise security, compliance, and risk management standards.

Cross-Functional Collaboration

• Partner with Enterprise Architects, Platform Engineering, Infrastructure, Security, Observability, and Development teams. 
• Participate in architecture reviews, design workshops, and Agile ceremonies. 
• Provide technical guidance throughout the SDLC from design through production deployment. 
• Validate architecture decisions and ensure adherence to enterprise reliability standards.
• Contribute operational insights that influence future platform architecture decisions.

Skills & Experience

Qualifications That Will Help You Thrive

Required Experience

• Bachelor’s Degree in Computer Science, Information Technology, Engineering, or related discipline. 
• 5+ years of experience in Solution Architecture, Site Reliability Engineering, Platform Engineering, DevOps, or Cloud Architecture. 
• Experience designing highly available, scalable, and resilient distributed systems. 
• Strong understanding of cloud infrastructure and platform architecture principles. 
• Experience supporting production operations and enterprise-scale technology environments. 
• Knowledge of observability, monitoring, logging, and telemetry frameworks. 
• Experience with Infrastructure as Code and deployment automation concepts. 
• Strong communication and stakeholder management skills.