Pricipal AI Solution Architect

Original job posting

Own end-to-end architecture for GenAI, agentic AI, RAG, intelligent document-processing, multimodal, and AI-enabled application solutions, while establishing reusable platform capabilities and standards. Personally evaluate technologies, build prototypes and reference implementations, contribute and review code, and resolve complex production issues across deployment, monitoring, evaluation, and governance.

Job details

Title: Principal AI Solution Architect: GenAI, Agentic AI & Platform Workplace type: Remote Working hours: PST 35 Hours work week on Contract The Principal AI Solution Architect owns the end-to-end technical architecture for a portfolio of GenAI, agentic AI, RAG, intelligent document-processing, multimodal and AI-enabled application solutions. The role connects near-term use-case delivery with a reusable platform direction so that teams do not repeatedly solve identity, integration, evaluation, deployment, monitoring, and governance in different ways. This is a deeply hands-on architecture role. The successful candidate personally researches and evaluates relevant technologies, creates technical spikes and reference implementations, contributes code to shared platform capabilities, reviews implementation across the platform and resolves complex issues through code, configuration, traces and working tests. Required qualifications Substantial experience owning architecture for production GenAI, RAG, agentic AI or intelligent automation systems in complex enterprises. Current hands-on proficiency in Python and API-based solution development, with sufficient depth to build, debug and review production-quality GenAI and agentic AI code. Demonstrated recent contribution to shared AI platform or application repositories, including reference implementations, code reviews, automated tests or production troubleshooting. Ability to validate architecture through Azure and Databricks environments, deployment configuration, traces, evaluation results and working code, not only design documentation. Current, practical knowledge of the evolving Microsoft AI ecosystem and recent Azure and Microsoft Foundry capabilities, supported by recent implementation, technical evaluation or production experience rather than certification alone. Experience implementing at least one production or production-relevant GenAI platform pattern and using it to deliver an end-to-end use case. Experience reviewing code across multiple contributors and converting architectural findings into specific, implementable changes. Demonstrated architecture depth across application, integration, data, cloud, security and operational concerns, not only model selection or infrastructure diagrams. Strong practical experience with Azure AI capabilities and Databricks, including the ability to validate decisions through working code or prototypes. Strong knowledge of agents, tools, MCP, retrieval systems, APIs, distributed systems, identity, observability, evaluation and production governance. Experience defining reusable platforms, reference architectures, technical standards and migration paths from experimentation to production. Ability to explain trade-offs clearly to engineering teams, business stakeholders, architecture authorities and risk/control partners. Strong written communication, decision documentation and technical leadership skills.

What you’ll do

Own end-to-end architecture for GenAI, agentic AI, RAG, intelligent document-processing, multimodal, and AI-enabled application solutions, while establishing reusable platform capabilities and standards. Personally evaluate technologies, build prototypes and reference implementations, contribute and review code, and resolve complex production issues across deployment, monitoring, evaluation, and governance.

Requirements

Requires substantial experience architecting production AI systems in complex enterprises, with current hands-on Python and API development skills and recent contributions to shared AI repositories. Candidates should have practical Azure AI and Databricks experience, strong knowledge of agents, retrieval, integration, security, observability, evaluation, and governance, and the ability to translate architecture decisions into implementable changes.

Listed skills

• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• GenAI Architecture
• Agentic AI
• Retrieval-Augmented Generation
• Azure AI
• Databricks
• API Development
• AI Platform Engineering
• Code Review
• Production Troubleshooting
• AI Evaluation
• Identity and Security
• Observability
• Distributed Systems
• Technical Leadership
• Architecture Documentation

Job areas

• Technology
• Software
• Data & Analytics
• Engineering
• Consulting