AI Architect
AI Architect
Posted 50 days ago
SENIORITY
Manager
About the role
- Competitive compensation
- Opportunity to influence enterprise‐wide AI architecture
- High visibility partnership with executive leadership
- Long‐term career growth in a collaborative, AI‐driven organization
- Comprehensive benefits package
- Health, dental and vision coverage 401(k) with company match
- Perks including employee discounts, financial wellness planning, tuition reimbursement and more
- Certified Great Place to Work and voted a 2019-2026 Computerworld Best Places to Work in IT
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, AI/ML or a related field
- Azure certifications (Solutions Architect, Azure AI Engineer) preferred7–12+ years of experience in AI/ML engineering, cloud architecture or enterprise software engineering
- Proven experience architecting and delivering production AI or ML solutions on Azure
- Experience with REST APIs, serverless functions, microservices and event-driven architectures
- Backend development in Python with working knowledge of C# or Node.js. Hands-on experience with Azure OpenAI, Azure Machine Learning, Azure AI Search, Microsoft Fabric and Lakehouse architectures
- Experience with embeddings, vector databases, RAG patterns, LangChain, Semantic Kernel and MLflowProficiency with Git, Azure DevOps CI/CD, Docker and Kubernetes
- Strong understanding of data modeling, governance, lineage and security
- Strong communication skills across technical and non-technical audiences
- Ability to translate business workflows into scalable technical architectures
- Strong ownership mindset with focus on reliability, cost optimization and long-term scalability
- Product mindset with ability to align AI architecture to business outcomes
- AI Strategy & Enterprise Architecture
- Evaluate and recommend AI models, APIs and platforms (e.g., Anthropic, OpenAI, Microsoft, Google) based on security, reliability, cost and enterprise fit
- Define the enterprise AI architecture across Azure OpenAI, Azure AI Search, Microsoft Fabric, Azure ML, APIs, event-driven systems and operator-facing tools
- Establish standards for building LLM applications, retrieval-augmented generation (RAG) systems, intelligent agents and ML models at scale
- Create reference architectures for AI-powered solutions including real-time workflows, automation, copilots and knowledge assistants
- Application Architecture & Integration
- Design how AI services integrate with core applications, including broker tools, APIs, workflows and backend services
- Establish patterns for serverless functions, microservices, REST APIs, event-driven pipelines and end-to-end orchestration
- Partner with application development teams to embed AI into product features with the right performance, security, authentication and data flow patterns
- Ensure AI solutions meet enterprise CI/CD, observability, reliability and SLA standards
- Solution Design & Technical Leadership
- Lead solution designs for AI platforms including vector databases, embedding pipelines, inference services, feature stores and model registries
- Translate complex operator workflows into scalable, AI-enabled architectures that improve decision-making and productivity
- Conduct architecture, design reviews and mentor AI Engineers, Software Engineers, Data Engineers and Data Scientists
- Data & Integration Architecture
- Partner with Data Engineering to ensure Fabric Lakehouse, Delta tables, warehouse layers and streaming systems support both training and inference workloads
- Architect and optimize RAG pipelines using Azure AI Search, vector indexing, embeddings and metadata strategiesMLOps, Governance & Operational Readiness
- Define and implement enterprise MLOps standards for model lifecycle management, versioning, monitoring and retraining
- Apply Responsible AI practices including content filtering, privacy, compliance and hallucination mitigation
- Ensure AI systems are observable with performance and cost monitoring
- Innovation & Continuous Improvement
- Evaluate emerging AI models, agent frameworks and Azure capabilities for use in logistics workflows
- Lead proofs of concept (PoCs) and accelerate adoption of high-value AI initiatives
- Develop reusable technical playbooks and architectural patterns to mature AI across engineering teams
Before you apply
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Your profile is current
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Two examples you can talk through
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A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
