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AI Architect with Azure Kubernetes Service Experience

iCUBE SolutionsUnited States🇺🇸United StatesPosted 2 Sept 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
United States
Posted
22 hours ago
ETLMachine LearningAzureDatabricksGenerative AIKubernetesPostgreSQLPythonStakeholder ManagementVault

Job Description

AI Architect with AKS Experience

Remote

6+ Months
Job Description:

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 QualificationsEducation & 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
Thanks,
Vinod

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