AI Solution Architect

Position Summary

NOVO is seeking an AI Solution Architect to serve as a dual-purpose technical leader—driving customer success through both pre-sales and post-sales engagements while driving internal innovations. This role sits at the intersection of customer engagement, technical architecture, and hands-on solution development, with Microsoft AI technologies as the primary platform. A core expectation of this role is to design and deliver secure AI solutions, delivering measurable ROI while meeting enterprise security, privacy, and compliance requirements.

You'll design enterprise-grade AI solutions, lead proof-of-concept initiatives, and build custom applications leveraging Microsoft Copilot, Azure AI Foundry, and Azure OpenAI Service. As an individual contributor at the senior level, you'll translate complex business requirements into scalable technical architectures that are secure-by-design, including threat modeling, least-privilege access, secure data handling, model/agent guardrails, and production monitoring. You'll accelerate delivery using cutting-edge auto-coding tools like GitHub Copilot, Claude Code, or equivalent while maintaining strong engineering hygiene including reviews, testing, and secure coding practices.

This position offers the rare opportunity to balance customer-facing technical leadership with internal “customer zero” innovation work, contributing directly to NOVO's competitive advantage in the rapidly evolving AI solutions market.

Pre-Sales Technical Architecture

Solution Design & Customer Discovery: Lead customer discovery and translate business, technical, and regulatory requirements into secure, end-to-end Microsoft AI solution architectures.

Proof-of-Concept Development & Demonstrations: Build and present secure, working POCs and demos that validate architecture, guardrails, and real-world feasibility for executive and technical audiences.

Technical Proposal Support: Partner with sales to produce technical proposals, architectures, roadmaps, and compliant RFP/RFI responses emphasizing secure and responsible AI.

Secure AI & Governance: Design and validate secure-by-design, responsible AI architectures with threat modeling, governance alignment, and production-ready safeguards.

Post-Sales Implementation & Technical Leadership

Solution Implementation & Configuration: Implement and configure scalable, secure full-stack AI solutions integrated with Microsoft and enterprise systems using least-privilege patterns.

Technical Leadership: Lead implementation work-streams, conduct architecture and security reviews, mitigate risk, and support monitored, optimized production deployments.

Knowledge Transfer & Customer Enablement: Deliver documentation, training, and ongoing technical advisory support to ensure successful customer adoption and long-term value.

Internal NOVO AI Innovation Framework Execution

Internal AI Solution Development: Architect and build secure internal AI accelerators and Agentic solutions aligned to NOVO’s framework, leveraging modern auto-coding and orchestration tools.

Innovation & Continuous Improvement: Continuously evaluate emerging Microsoft AI capabilities and industry best practices to evolve NOVO’s AI offerings and innovation framework.

Citizenship & Location

Candidates must be US Natural-born citizens currently residing within the US. 

NOVO will perform a detailed background check (including FBI) prior to employment. DO NOT APPLY FOR THIS POSITION IF YOU DO NOT MEET THIS REQUIREMENT. 

Education & Professional Experience

• Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or related technical field.

• 5-8 years of professional experience in solution architecture, with recent experience in AI/ML implementation, cloud solutions, or enterprise software development.

• 3+ years working directly with cloud platforms (Azure strongly preferred) and AI/ML technologies in production environments.

Required Certifications

• Microsoft Certified: Azure AI Engineer Associate (AI-102) – Current certification, or ability to obtain within 90 days of employment.

• Ability to pursue additional Microsoft certifications as technologies evolve.

Core Technical Skills

Microsoft AI Platform Expertise

• Azure AI Foundry: Hands-on experience with model catalog, prompt flow and agent orchestration, evaluation tools, and managed deployment.

• Microsoft Copilot Studio: Experience building custom copilot agents, configuring knowledge sources (SharePoint, OneDrive, websites), integrating Power Automate flows, and deploying across multiple channels

• Azure AI Services: Working knowledge of Azure AI Search (vector search, semantic ranking, RAG/GraphRAG patterns), Computer Vision, Speech Services, Document Intelligence, and Content Safety

Development & Auto-Coding Proficiency

• Proficiency with AI-assisted development tools: GitHub Copilot, Claude Code, OpenAI Codex, or equivalent AI coding assistants with demonstrated productivity gains

• Programming languages: Proficiency in Python, JavaScript, or C#; experience with AI/ML frameworks (i.e. Microsoft Agent Framework, Semantic Kernel, AutoGen, LangChain, Prompt Flow)

• Cloud architecture: Deep understanding of Azure services including App Service, Azure Functions, Logic Apps, API Management, Azure Kubernetes Service (AKS), and container deployments

Data & Integration Skills

Secure AI (Security, Privacy & Responsible AI)

• Identity & access: Experience designing least-privilege access for AI apps/agents (Entra ID, RBAC, managed identities/service principals) and securing secrets/keys.

• Secure GenAI patterns: Practical knowledge of common GenAI threats (prompt injection, data exfiltration, insecure plugins/tools) and mitigation (input/output filtering, tool allow-lists, grounding, and sandboxing where applicable).

• Data protection: Understanding of enterprise data handling requirements (classification, retention, DLP) and how they apply to grounded agent architectures.

• Evaluation & monitoring: Ability to define and implement safety/security evaluations (red-teaming, abuse testing) and production monitoring for drift, abuse, and regressions.

• Compliance collaboration: Comfort partnering with security, compliance, and legal stakeholders to meet customer and regulatory obligations.

• Data integration: Experience with data integration patterns including REST APIs, webhooks, event-driven architectures, and message queues

• Data platforms: Knowledge of relational, vector and NoSQL databases (Azure SQL, Cosmos DB, PostgreSQL)

• Data architecture: Understanding of data modeling, ETL/ELT pipelines, data governance, and secure AI best practices (access controls, privacy-by-design, audit ability).

Preferred Qualifications

• Advanced Azure certifications: Azure Solutions Architect Expert, Azure AI Fundamentals, Power Platform certifications

• AI/ML specializations: Experience with MLOps practices, model monitoring, fine-tuning techniques, production RAG (or equivalent) architectures

• Enterprise integration experience: Familiarity with ERP, CRM, Service Management, Dynamics 365, or other enterprise SaaS platforms

• Vector databases: Experience with vector storage and retrieval systems (Azure AI Search)

• Container orchestration: Kubernetes, Docker, Azure Container Apps, or OpenShift experience

• Security & compliance expertise: Knowledge of Microsoft Purview, Entra ID (Azure AD), data loss prevention (DLP), GDPR, SOC 2, and responsible/secure AI practices (threat modeling, guardrails, and audit ability).

• Security credentials: Security-focused certifications or training (e.g., Azure Security Engineer Associate, SC-900, AZ-500) and/or experience operating within secure SDLC practices.

Essential Soft Skills & Collaboration Abilities

• Communication Excellence: Clearly translate complex AI concepts to business audiences, demonstrate executive presence, and actively listen to uncover explicit and implicit customer needs.

• Stakeholder Management: Effectively navigate cross-functional stakeholders, resolve conflicts, and drive consensus through influence rather than formal authority.

• Problem-Solving & Critical Thinking: Analyze complex business problems, design creative and feasible AI solutions, and proactively identify risks and constraints.

• Adaptability & Continuous Learning: Rapidly adopt new technologies, operate effectively amid ambiguity, and continuously evolve skills in a fast-changing AI landscape.

• Collaboration & Customer Focus: Collaborate across disciplines with a customer-centric mindset while mentoring others and contributing to shared organizational knowledge.