Why This Role Stands Out
This remote Senior Principal SRE Architect role offers a unique opportunity to shape critical infrastructure and drive innovation in AI-driven solutions, making it ideal for seasoned leaders passionate about operational resilience and cutting-edge technology. You'll leverage your extensive full-stack, cloud, and DevOps expertise to build robust systems and mentor teams, contributing to a company known for its forward-thinking approach. This is an exciting chance to make a significant impact and grow your career.
Quick Overview
Job Description
Job Title: Senior Principal SRE Architect Location : Dallas or St Louis or Remote Duration: 6+ Months
This is a strategic and technical leadership role focused on operational resilience, automation, and architectural oversight.
Core Technical Skills
Full Stack & Architecture Expertise: Ability to provide guidance across front-end, backend, APIs, databases, and network layers.
Cloud Platforms & Infrastructure: Hands-on experience with major cloud providers (AWS, Azure, Google Cloud) and their AI-specific services. This includes knowledge of containerization, Kubernetes, and micro-services.
DevOps & CI/CD: Advanced knowledge of continuous integration/delivery pipelines, configuration as code, and deployment automation.
Observability & Monitoring: Proficiency in logging, alerting, dashboarding tools, and standards for operational readiness.
IT Service Management (ITSM): Strong understanding of ITSM practices and compliance frameworks.
Operational Readiness: Expertise in chaos engineering, production readiness, and risk mitigation strategies.
Network Concepts: Wireshark, TCP/IP, Routing Protocols, Firewalls, Load balancers, Proxies, FTP/SFTP, Automation & Scripting
Certificate management: Venafi, ECMS, CerTIS, OpenSSL, cipher suites
Machine Learning (ML) & Deep Learning: Deep understanding of various ML algorithms, neural networks, and frameworks such as TensorFlow, PyTorch
AI Architecture & Pipeline Planning: The ability to design the overall workflow and architecture for ML and deep learning workloads, considering scalability, fault tolerance, cost-efficiency, and security.
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