Principal AI Solution Architect – GenAI, Agentic AI & Azure

• Location: No Location Set
• Type: Contract
• Job #35695

Principal AI Solution Architect – GenAI, Agentic AI & Azure

Contract: 6 Months + Extension
Location: 100% Remote – North or South America
Hours: 37.5 Hours per Week
Start: Early September

Position Overview

We are seeking a Principal AI Solution Architect to provide hands-on technical and architectural leadership across a portfolio of enterprise Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), intelligent document processing, multimodal AI, and AI-enabled application initiatives.

This is a deeply technical, hands-on architecture role for someone who can define enterprise AI architecture while also proving architectural decisions through working code, technical spikes, reference implementations, testing, and deployment evidence.

The successful candidate will own end-to-end AI solution architecture, including target-state and transition-state architectures, reusable platform patterns, shared services, integration boundaries, security, observability, evaluation, identity, governance, and production-readiness standards.

The environment has a strong emphasis on Microsoft Azure, Microsoft AI/Foundry capabilities, and Databricks. The Architect will work closely with engineering, AI/ML, data, platform, security, and business teams to ensure AI solutions can progress from proof of concept through scalable production deployment without fundamental redesign.

This is not a diagram-only or advisory architecture position. The successful candidate must be comfortable moving between strategic architecture, detailed technical design, code and pull-request reviews, troubleshooting, prototyping, and hands-on implementation.

Key Responsibilities

AI Solution & Platform Architecture

• Own the end-to-end technical architecture across GenAI, RAG, Agentic AI, intelligent document processing, multimodal AI, and AI-enabled application solutions.
• Define target-state, transition-state, and use-case architectures supporting enterprise AI initiatives.
• Establish clear platform, application, integration, and shared-service boundaries.
• Design reusable architectural patterns for:
  • Model and endpoint access
  • RAG and enterprise retrieval
  • AI agents, tools, and orchestration
  • APIs and gateways
  • Model/service registries
  • Evaluation and testing
  • Observability and tracing
  • Feedback mechanisms
  • Identity and authorization
  • Human oversight and approval
• Ensure architecture supports reuse across multiple AI use cases rather than isolated point solutions.

Azure, Databricks & AI Platform Engineering

• Provide architectural leadership across Microsoft Azure, Microsoft AI/Foundry capabilities, and Databricks.
• Research and validate current platform capabilities to determine appropriate technical patterns and services.
• Build technical spikes, proofs of concept, and deployable reference implementations to validate key architectural decisions.
• Materially contribute to the development of reusable Agentic AI and GenAIOps platform capabilities through code and configuration.
• Evaluate platform capabilities, limitations, integration options, and architectural trade-offs using working implementations and technical evidence.

Production-Ready Enterprise AI

• Define non-functional requirements across:
  • Security and privacy
  • Identity and authorization
  • Traceability and auditability
  • Scalability and resilience
  • Performance and latency
  • Recovery and availability
  • Observability and monitoring
  • Supportability
  • Cost and operational efficiency
• Ensure AI solutions can transition from experimentation and proof of concept into secure, scalable production environments.
• Establish architecture and engineering patterns that minimize redesign as AI use cases mature.

Hands-On Engineering & Technical Validation

• Personally develop and validate reference architectures and representative implementations.
• Use tests, traces, benchmarks, deployment results, and technical evidence to support important architecture decisions.
• Implement or materially co-implement at least one representative AI use case using the reusable platform foundation.
• Troubleshoot complex issues spanning:
  • Applications
  • AI agents
  • Models
  • Retrieval and RAG
  • Data
  • Identity
  • APIs and integrations
  • Cloud/platform services
• Work directly with engineering teams to resolve architectural and implementation challenges.

Architecture Governance & Code Quality

• Review code and pull requests across AI platform and use-case repositories.
• Assess implementations for:
  • Architectural conformance
  • Modularity and reuse
  • Security
  • Reliability
  • Observability
  • Maintainability
  • Appropriate platform utilization
• Provide actionable, code-level technical feedback rather than architecture guidance in isolation.
• Establish reusable standards and patterns that engineering teams can consistently adopt.

Technical Leadership & Client Engagement

• Lead architecture discussions with technical teams, business stakeholders, and client leadership.
• Translate complex AI concepts and technical trade-offs into clear recommendations and actionable decisions.
• Work across strategic, architectural, and engineering levels depending on the needs of the initiative.
• Provide technical direction while remaining actively involved in implementation and delivery.
• Help guide AI initiatives from concept and experimentation through production deployment and operationalization.

Required Qualifications

• Senior/Principal-level experience designing and delivering enterprise AI and cloud-based solution architectures.
• Strong hands-on experience with Generative AI and modern AI application architecture.
• Practical experience designing or implementing Agentic AI and/or RAG-based solutions.
• Strong experience with Microsoft Azure and Azure-based enterprise architecture.
• Experience working with Databricks in enterprise data, analytics, ML, or AI environments.
• Strong understanding of APIs, integration patterns, identity, authorization, security, observability, and distributed application architecture.
• Demonstrated ability to move from architecture and technical design into hands-on implementation and validation.
• Experience developing proofs of concept, technical spikes, reference implementations, or reusable platform components.
• Ability to review code and provide meaningful code-level architectural and engineering guidance.
• Strong understanding of production AI considerations including security, privacy, scalability, resilience, traceability, latency, monitoring, supportability, and cost.
• Strong troubleshooting skills across AI, application, cloud, data, identity, and integration layers.
• Excellent client-facing communication and technical leadership skills.
• Ability to work effectively with architects, AI/ML engineers, software engineers, data engineers, platform teams, security teams, and business stakeholders.

Highly Relevant Experience

Experience in several of the following areas would be particularly valuable:

• Microsoft Foundry / Azure AI services
• Databricks AI/ML capabilities
• Generative AI / LLM applications
• Agentic AI architectures and orchestration
• Retrieval-Augmented Generation (RAG)
• Intelligent document processing
• Multimodal AI
• AI evaluation and testing frameworks
• GenAIOps / LLMOps / MLOps
• Model and endpoint management
• AI observability, tracing, and monitoring
• Enterprise identity and access management
• Human-in-the-loop / human oversight patterns
• AI platform engineering and reusable AI services
• Enterprise financial-services environments

What Success Looks Like

The successful Architect will be able to lead the architectural conversation and prove the architecture through working implementation.

You should be equally comfortable defining an enterprise AI target state, evaluating Azure and Databricks capabilities, designing reusable AI platform patterns, reviewing implementation code, troubleshooting a RAG or agent workflow, and working directly with engineers to deliver a production-ready solution.

The objective is not simply to design individual AI use cases, but to create a secure, scalable and reusable enterprise AI foundation that enables multiple GenAI and Agentic AI initiatives to move efficiently from experimentation into production.