Architect - AI platforms Implementation and Integration

Responsibilities

• Consults with the business and operations to identify and understand challenges and opportunities to improve the use and effectiveness of technology, find creative solutions through knowledge of the domain, practical experiments, and Proof of Concepts, and aligns the solution intent and architectural vision to an evolving backlog of technology change.
• Works with product management and other stakeholders to plan and prioritize technology focused backlog items for the architecture runway to enable business epics/features.
• Performs design and code reviews to ensure all non-functional requirements for a solution are sufficiently met (for example, security, performance, maintainability, scalability, usability, and reliability).
• Led rapid shaping of a high level architecture with details filled in with emerging business requirements and ensures architecture is flexible, modular, and designed to adapt easily.
• Edits the architecture and assists with system design for the development teams to support implementation and provides solution options to resolve any architectural impediments.
• Uses the defined best practices, templates, and documentation to create architectural designs and suggests improvements to best practices and templates.
• AI Architecture Implementation & Enablement:
  • Implement and refine AI and data architecture solutions.
  • Deploy, configure, and operate vendor AI platforms and services within on-prem OpenShift clusters.
  • Support implementation of Generative AI solutions including LLM services, RAG pipelines, agents, and supporting data services.
  • Apply standardized architectural patterns for scalability, reliability, and security.
• Application of AI Controls & Responsible AI Standards:
  • Implement predefined AI control standards related to security, privacy, access management, logging, and monitoring.
  • Embed established & new Responsible AI mechanisms such as content filtering, prompt controls, telemetry, and explainability tooling.
  • Ensure deployed solutions align with enterprise requirements for auditability, lineage, and approved usage patterns.
• Systems Integration & Delivery:
  • Build, configure, and test integrations between AI platforms and enterprise systems including data platforms, identity services, monitoring tools, and other AI capabilities.
  • Develop and maintain APIs, data pipelines, and event-driven integrations to support end-to-end AI solutions.
  • Document technical implementations, deployment configurations, and operational dependencies.
  • Support production readiness activities including validation, performance testing, observability configuration, and operational handoff.
  • Troubleshoot deployment, runtime, and integration issues across AI platform components.
  • Provide implementation feedback and identify technical gaps or improvement opportunities to Senior Architects.

Qualifications

• 4–7 years of experience in data engineering, AI/ML platforms, platform engineering, or architecture-adjacent roles.
• Hands-on experience deploying and operating applications on Kubernetes or OpenShift platforms.
• Experience implementing system integrations using APIs, data pipelines, and event-based architectures.
• Working knowledge of AI/ML and Generative AI platforms, components, and deployment patterns.
• Proficiency in Python and SQL; familiarity with containerized workloads.
• Understanding of enterprise security, identity, logging, and operational requirements.
• Strong execution, problem-solving, and collaboration skills.
• Hands-on AI platform implementation and integration.
• Kubernetes / OpenShift deployment expertise.
• Application of architectural standards and AI controls.