Principal AI Solution Architect – GenAI, Agentic AI & Azure (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.

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