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Solutions AI Architect with Strong Azure Databricks

Cloud Destinations LLCUnited States🇺🇸United StatesPosted 2 Sept 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
United States
Posted
Yesterday
ETLMachine LearningAzureDatabricksGenerative AIKubernetesPostgreSQLPythonStakeholder ManagementVault

Job Description

Position Title: Solutions AI Architect with Strong Azure Databricks
Duration: 12+ months Contract
Client Location: Remote
Job Details:
Solutions AI Architect
Position Overview
  • We are seeking an experienced AI Architect to lead the design and delivery of AI-enabled capabilities across a portfolio of enterprise digital products. This is a highly hands-on architecture role that combines AI/ML prototyping, solution architecture, technical governance, and cross-functional leadership.
  • The ideal candidate will have strong experience developing AI/ML solutions in Python and Azure Databricks, architecting solutions across multiple products, and deploying applications within Azure Commercial environments, particularly using Azure Databricks, PostgreSQL, and Azure Kubernetes Service (AKS).
  • This role is approximately 50% hands-on AI/ML development and prototyping, making practical implementation experience as important as architecture expertise.
Key Responsibilities
AI/ML Solution Development
  • Design, prototype, and validate AI/ML capabilities using Python and Azure Databricks.
  • Develop and evaluate Generative AI solutions using the OpenAI API, as well as traditional machine learning, forecasting, optimization, and predictive modeling approaches.
  • Translate business and operational requirements into scalable AI/ML prototypes and production-ready solution designs.
  • Evaluate model performance, technical feasibility, scalability, cost, and operational considerations.
  • Transition successful prototypes and proof-of-concepts to engineering and implementation teams. Enterprise Solution Architecture
  • Lead end-to-end architecture for AI-enabled capabilities from concept and prototype through implementation handoff.
  • Design scalable, secure, maintainable solutions across a portfolio of interconnected digital products.
  • Evaluate architecture tradeoffs involving cost, performance, security, data governance, scalability, and delivery timelines.
  • Establish architectural patterns, technical standards, and reusable approaches across multiple product teams.
  • Identify opportunities for shared services, reusable components, and technology standardization. Cross-Product Architecture & Governance
  • Review solutions across multiple product teams to identify duplicated, conflicting, or inconsistent architectural approaches.
  • Drive consistency across application, data, AI/ML, orchestration, planning, scheduling, and workflow capabilities.
  • Establish and maintain architecture standards, design patterns, and technical guidelines.
  • Participate in architecture and responsible-AI governance processes.
  • Prepare architecture documentation, technical recommendations, decision records, and review materials.
  • Address architecture review feedback and work with stakeholders to resolve technical issues. Technology Enablement
  • Partner with engineering, data, cybersecurity, infrastructure, and product teams to translate AI and data opportunities into supportable enterprise capabilities.
  • Provide technical guidance on Azure Databricks, PostgreSQL, AKS, AI/ML platforms, APIs, data architectures, and cloud-native technologies.
  • Ensure proposed solutions align with enterprise security, data governance, infrastructure, and operational requirements.
  • Support development teams through architecture decisions, technical challenges, and implementation handoffs. Leadership & Mentorship
  • Mentor architects, software engineers, data engineers, and data scientists on AI, ML, data engineering, and solution architecture practices.
  • Promote engineering best practices, reusable patterns, and effective AI/ML development methodologies.
  • Communicate complex technical concepts clearly to both technical and business stakeholders.
  • Influence technical direction across multiple teams and product areas.
Required Qualifications - Education & Experience
  • Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, or a related field, or equivalent professional experience.
  • 8+ years of progressive experience in IT, software engineering, digital products, AI/ML, or solution architecture; 10+ years preferred.
  • Demonstrated experience in hands-on AI/ML development as well as enterprise solution architecture
  • Required Technical Skills
  • Strong Python development experience for AI/ML prototyping and solution development.
  • Must have strong hands-on experience with Azure Databricks, including AI/ML development and data processing.
  • Experience developing forecasting, optimization, predictive analytics, or traditional machine learning models.
  • Experience integrating Generative AI through the OpenAI API.
  • Strong Azure Commercial experience in enterprise environments.
Hands-on experience with:
  • Azure Databricks Must Have
  • Azure Kubernetes Service (AKS)
  • PostgreSQL
  • Azure DevOps
  • Experience designing solutions for secure, controlled, or highly governed cloud environments.
  • Comfortable working within GitHub Copilot-assisted development workflows. Preferred Qualifications
  • Experience with Data Vault or Medallion architecture.
  • Experience with modern ETL/ELT platforms and data integration tools, such as Fivetran.
  • Experience with enterprise AI governance and responsible-AI review processes.
  • Experience designing multi-product or portfolio-level architecture.
  • Experience transitioning AI/ML proofs-of-concept into production implementations.
  • Experience with AI orchestration, workflow automation, or enterprise GenAI platforms.
Core Competencies
  • Enterprise and solution architecture
  • Artificial intelligence and machine learning
  • Generative AI
  • Python development
  • Azure Databricks
  • Azure cloud architecture
  • Kubernetes / AKS
  • Data architecture and engineering
  • Technical governance
  • Architecture standards and design patterns
  • Stakeholder management
  • Technical leadership and mentoring
  • Responsible AI and data governance
  • Strong written and verbal communication

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