This is a remote position.
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.
Requirements
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
• Translate technical complexity: Ability to explain sophisticated AI architectures, model behaviors, and technical trade-offs to non-technical business stakeholders in clear, accessible language.
• Executive presence: Confidence and credibility when presenting technical solutions to C-level executives, delivering compelling value propositions and business justifications.
• Active listening: Strong ability to understand stated and unstated customer needs, pain points, and business objectives through discovery conversations.
Stakeholder Management
• Multi-stakeholder navigation: Skill in managing relationships across engineering teams, product managers, sales organizations, customer executives, and technical teams.
• Conflict resolution: Ability to mediate technical disagreements, find common ground when priorities differ, and maintain project momentum.
• Influence without authority: Capability to gain buy-in and drive consensus across cross-functional teams where you don't have direct management authority.
Problem-Solving & Critical Thinking
• Analytical mindset: Strong ability to decompose complex business problems into technical requirements, identify solution patterns, and design appropriate architectures.
• Creative solution design: Capacity to design innovative AI solutions that address unique customer challenges while balancing technical feasibility, cost, and time constraints.
• Risk assessment: Skill in identifying technical risks, dependencies, security considerations, and constraints early in solution design.
Adaptability & Continuous Learning
• Rapid technology adoption: Ability to learn quickly and apply new AI technologies, frameworks, model architectures, and Microsoft platform updates in a fast-moving landscape.
• Comfort with ambiguity: Capability to make informed decisions with incomplete information and adjust approaches as new insights emerge.
• Growth mindset: Commitment to continuous learning, experimentation, and staying current with AI industry trends, research, and best practices.
Collaboration & Customer Focus
• Cross-functional collaboration: Experience working effectively with developers, data engineers/scientists, project managers, business analysts, and sales professionals.
• Customer-centric approach: Strong empathy for customer challenges and dedication to delivering solutions that create measurable business value.
• Knowledge sharing: Willingness to mentor colleagues, conduct training sessions, document learnings, and contribute to organizational knowledge.
