The company, a global leader in thermal management, mission-critical building systems, energy efficiency, and decarbonization, helps customers use energy more productively, reduce carbon emissions, and operate with the precision and resilience required in rapidly expanding industries such as data centers, healthcare, pharmaceuticals, advanced manufacturing, and higher education.
For more than 140 years, the company has delivered performance where it really matters.
Requirements
Required experience
Bachelor's degree in a relevant discipline, or equivalent practical experience.
8+ years in solution architecture, software engineering, cloud architecture or enterprise technology roles.
Proven experience designing and delivering production Azure solutions in complex enterprise environments.
Hands-on architecture experience with generative AI, LLMs, RAG, conversational AI, document intelligence or agent-based solutions.
Ability to guide solutions from discovery through design, delivery, governance approval and operational support.
Technical Expertise
AI Architecture
Generative AI, Agentic AI, RAG (Retrieval-Augmented Generation), enterprise search, document intelligence, evaluation, and AI safety.
Azure AI
Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning, and approved model endpoints.
Integration
REST APIs, Azure API Management, event-driven architectures, messaging platforms, tool integration, and enterprise integration patterns.
Security
Microsoft Entra ID, managed identities, OAuth/OIDC, RBAC, private networking, secrets management, and secure-by-design principles.
Governance
Architectural decision records, Responsible AI, privacy, risk management, compliance, observability, and production readiness.
Leadership and ways of working
Strong systems thinking and judgement to balance innovation, speed, security, quality and maintainability.
Clear communication and ability to influence across technical, product and business teams.
Comfort facilitating workshops, resolving ambiguity and presenting options with trade-offs.
Preferred qualifications
Experience with Microsoft Agent Framework, Semantic Kernel, LangGraph, LangChain or similar orchestration frameworks.
Experience with vector-capable data services such as Cosmos DB, Redis, PostgreSQL/pgvector or similar.
Familiarity with Terraform, Kubernetes, Azure Container Apps, AKS, Azure API Management and landing-zone patterns.
Azure Solutions Architect Expert, Azure AI Engineer Associate, TOGAF or comparable certification preferred.
What success looks like
AI teams move from validated use case to production faster using clear, approved solution patterns.
Solutions meet standards for security, privacy, Responsible AI, resilience, observability and supportability.
Stakeholders understand decisions, risks, dependencies, trade-offs and expected business outcomes.
SALARY RANGE: $140,000 - $160,000 (Salary to be determined by the education, experience, knowledge, skills, and abilities of the applicant, internal equity, and alignment with market data.) This role offers a competitive Bonus plan that will take into account individual, group, and corporate performance. This position includes a competitive benefits package. The posted salary range reflects the target compensation for this role. However, we recognize that exceptional candidates may bring unique skills and experiences that exceed the typical profile. If you believe your background warrants consideration beyond the stated range, we encourage you to apply. To support an efficient and fair hiring process, we may use technology assisted tools, including artificial intelligence (AI), to help identify and evaluate candidates. All hiring decisions are ultimately made by human reviewers. For details, please visit the About Us tab on the company Careers site at the company's site
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Benefits
Competitive salary
Paid vacation/holidays/sick time
Comprehensive benefits package including 401K, medical, dental, and vision care
On the job/cross training opportunities
Encouraging and collaborative team environment
Dedication to safety through our Zero Harm policy
Job overview
The company International is seeking an AI Solution Architect to design and govern enterprise AI solutions across generative AI, agentic AI, machine learning, RAG, enterprise search and intelligent automation.
You will work with AI engineers, platform engineers, software teams, data scientists, product teams, cyber security, enterprise architecture and business stakeholders to turn AI opportunities into secure, scalable and supportable solutions.
How you will do it
Enterprise AI solution architecture
Lead architecture and solution design for enterprise AI initiatives across business domains.
Translate business needs into end-to-end solution architectures, roadmaps and non-functional requirements.
Define reusable reference architectures, integration patterns and clear architecture decisions.
Generative AI and agentic solutions
Design solutions using Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning, vector stores and approved enterprise APIs.
Define patterns for RAG, conversational AI, document intelligence, multimodal workflows and AI agents.
Establish approaches for model selection, evaluation, safety, grounding, cost and operational reliability.
Platform alignment and engineering collaboration
Align solutions with approved platform capabilities, deployment patterns, networking and operational controls.
Ensure AI traffic follows enterprise API management patterns for authentication, observability, throttling and cost allocation.
Partner with engineering teams on APIs, data flows, application boundaries, reviews and production readiness.
Security, governance and Responsible AI
Embed security, privacy, accessibility, compliance and Responsible AI requirements from the outset.
Define identity, RBAC, private networking, secrets, data protection, content safety and auditability patterns.
Document risks, assumptions, dependencies, mitigations and operational-readiness requirements.
Business engagement and technical leadership
Facilitate discovery and design workshops that turn ambiguous needs into phased delivery options.
Explain AI concepts, risks and trade-offs clearly to technical and non-technical stakeholders.
Provide technical leadership through reviews, mentoring and pragmatic architecture decisions.