Principal - Architecture

Job Title :Principal AI Platform Architect

Location :Dallas, TX(Remote)

Job Description :

Summary

We are seeking a highly experiencedPrincipal AI Platform Architect to lead the design, development, and operationalization of enterprise-scale AI platforms and agentic AI solutions. This role requires a hands-on technology leader with deep expertise inGenerative AI, Agentic Systems, Cloud Architecture, Platform Engineering, DevOps, and AI Operations (AIOps).

The ideal candidate will combine strong software engineering skills with modern AI platform expertise to build scalable, secure, and production-ready AI capabilities leveraging AWS and other cloud technologies. The individual will collaborate with enterprise architecture, engineering, operations, security, and business stakeholders to drive AI-powered transformation initiatives across the organization.

This is a hands-on architecture role requiring active contribution to solution design, coding, platform development, governance, and enterprise adoption of AI technologies.

Key Responsibilities

AI & Agentic Platform Architecture

• Design and implement enterprise AI platforms supporting GenAI, RAG, Agentic AI, and AI-powered automation workloads.
• Architect multi-agent systems leveraging AWS Bedrock, AgentCore, MCP (Model Context Protocol), and modern agent orchestration frameworks.
• Define enterprise AI reference architectures, reusable frameworks, and technical standards.
• Lead solution design for AI-powered copilots, assistants, intelligent workflows, and autonomous agents.
• Establish AI governance, security, observability, and operational frameworks.

AI Engineering & Application Development

• Build and deploy production-grade AI applications leveraging Large Language Models (LLMs) and foundation models.
• Develop Retrieval-Augmented Generation (RAG) solutions using vector databases and enterprise knowledge repositories.
• Design APIs, microservices, and event-driven architectures supporting AI workloads.
• Develop scalable backend services utilizing Python, Go, Java, or .NET technologies.
• Implement AI model lifecycle management practices including deployment, monitoring, evaluation, and continuous improvement.

Cloud & Platform Engineering

• Architect and implement cloud-native AI platforms on AWS, Azure, and/or GCP.
• Design highly available and secure platforms leveraging containerized and serverless architectures.
• Build self-service AI capabilities enabling rapid onboarding of development teams.
• Lead enterprise cloud modernization and AI transformation initiatives.
• Establish infrastructure automation and platform governance standards.

DevOps, MLOps & Automation

• Design and implement CI/CD pipelines for AI and cloud-native applications.
• Automate platform provisioning using Terraform, CloudFormation, Infrastructure as Code (IaC), and GitOps methodologies.
• Implement AI application deployment strategies, environment management, and release governance.
• Drive engineering productivity through adoption of AI-assisted development tools and modern software delivery practices.

Observability & AIOps

• Implement enterprise observability frameworks covering AI applications, infrastructure, and distributed systems.
• Design monitoring, tracing, and telemetry solutions leveraging OpenTelemetry and industry-leading observability platforms.
• Build operational intelligence capabilities using AIOps solutions and predictive analytics platforms.
• Define AI application monitoring standards including prompt tracing, model performance tracking, token consumption analysis, and operational diagnostics.
• Improve reliability, resilience, and operational efficiency of business-critical platforms.

Leadership & Stakeholder Management

• Collaborate with enterprise architecture, cybersecurity, operations, engineering, and business teams.
• Provide technical leadership and mentorship to architects and engineering teams.
• Drive architecture reviews, technology selection, governance discussions, and executive-level communications.
• Influence enterprise AI strategy, platform roadmaps, and transformation programs.

Required Technical Skills

Programming Languages

• Python (Mandatory)
• Go
• Shell Scripting
• Java (Preferred)
• .NET

AI & Generative AI Technologies

• Generative AI (GenAI)
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• AgentCore
• Model Context Protocol (MCP)
• AWS Bedrock
• Amazon SageMaker
• Vector Databases
• Model Lifecycle Management
• Agentic AI Frameworks and Multi-Agent Architectures

AI Coding Assistants / Copilots

• Kiro
• GitHub Copilot
• Amazon Q or comparable AI coding assistants

Cloud Platforms

• AWS (Strong preference)
• Microsoft Azure
• Google Cloud Platform (GCP)

DevOps & Automation

• Terraform
• CloudFormation
• GitHub Actions
• GitLab CI/CD
• Jenkins
• ArgoCD
• FluxCD
• Azure DevOps

Container & Platform Engineering

• Kubernetes
• Amazon EKS
• Docker
• Helm
• Istio
• Service Mesh Technologies

AIOps Platforms

• Moogsoft
• BigPanda
• Dynatrace Davis AI
• Splunk ITSI
• AI-driven Event Correlation Platforms
• Predictive Analytics Solutions

Monitoring & Observability

• Datadog
• Dynatrace
• Splunk
• New Relic
• Grafana
• Prometheus
• Elastic Stack
• OpenTelemetry

Required Experience

• 12+ years of experience in AI, Cloud Engineering, DevOps, Site Reliability Engineering (SRE), Platform Engineering, or related technology domains.
• 3+ years of hands-on experience designing and implementing AI/GenAI solutions in production environments.
• 5+ years of experience designing enterprise-scale platforms and cloud-native architectures.
• 3+ years of experience implementing AIOps, observability, or operational intelligence solutions.
• Proven experience delivering enterprise cloud transformation and modernization programs.
• Experience supporting mission-critical, highly regulated, or large-scale enterprise environments.
• Strong understanding of AI/ML operational frameworks, platform governance, and deployment best practices.
• Demonstrated experience leading cross-functional initiatives involving architecture, engineering, operations, and security teams.
• Experience implementing production-grade RAG, agentic AI, and AI platform solutions preferred.

Preferred Qualifications

• AWS Solutions Architect Professional Certification
• AWS AI/ML Specialty Certification
• Experience within Financial Services, Insurance, Healthcare, Telecommunications, or other regulated industries.
• Experience building enterprise AI Centers of Excellence (CoE).
• Experience defining AI governance, responsible AI, and model risk management frameworks.