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Senior AI Platform Integration Specialist

TalentBridgeDenver, CO🇺🇸United StatesPosted 8 Sept 2026

Why This Role Stands Out

This hybrid role offers a fantastic opportunity to shape the future of enterprise AI by defining integration standards and reusable architecture patterns, making you a key player in scaling AI capabilities. You'll thrive here if you're an experienced integration architect passionate about building secure, scalable, and well-governed AI solutions across diverse platforms. Apply now to make a significant impact and advance your career in AI integration.

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Denver, CO, United States
Posted
Yesterday
AWSSnowflakeDatabricks

Job Description

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Senior AI Platform Integration Specialist

We are seeking a Senior AI Platform Integration Specialist to help define and scale enterprise AI capabilities across a complex technology ecosystem. This individual will establish integration standards, reusable architecture patterns, and platform governance to ensure AI/ML solutions are securely and effectively consumed across enterprise applications, workflows, and business processes.

Responsibilities

  • Design enterprise integration patterns across AI/ML platforms, data platforms, APIs, applications, and workflow systems.
  • Define how AI outputs, including recommendations, alerts, scores, embeddings, agents, and AI services, are exposed, governed, monitored, and consumed by business-facing systems.
  • Develop reusable reference architectures, standards, and decision frameworks that support scalable AI adoption.
  • Partner with enterprise architecture, cybersecurity, data, platform, product, and business teams to align AI initiatives with enterprise standards.
  • Establish platform boundaries and integration strategies across Databricks, AWS, Snowflake, Palantir, BI tools, APIs, and custom applications.
  • Drive security, lineage, observability, auditability, reliability, and operational support requirements across AI integrations.
  • Eliminate one-off solutions by creating reusable patterns, integration guardrails, and governance models.

Required Qualifications

  • Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent experience.
  • Proven experience designing enterprise integration architectures across AI/ML platforms, data platforms, APIs, applications, and workflow technologies.
  • Strong understanding of AI/ML platform architecture, data governance, workflow orchestration, and enterprise application integration.
  • Experience designing production-grade integration patterns for model outputs, recommendations, alerts, embeddings, AI agents, and AI services.
  • Hands-on expertise with API design, event-driven architecture, service integration, identity and access management, data contracts, lineage, observability, and auditability.
  • Experience building reusable reference architectures and enterprise integration standards.
  • Ability to collaborate across technical, product, cybersecurity, architecture, platform, and business teams.
  • Experience evaluating platform fit-for-purpose and defining responsibilities across Databricks, AWS, Snowflake, Palantir, BI tools, and enterprise applications.
  • Ability to translate architecture strategy into practical implementation standards and governance frameworks.

Palantir & Enterprise AI Requirements

  • Experience architecting or governing integrations with Palantir Foundry, AIP, or similar enterprise workflow and operational decision platforms.
  • Strong knowledge of Palantir interoperability, API-based integrations, security controls, lineage, governance, and operational standards.
  • Experience defining how AI/ML outputs are consumed within enterprise workflow and decision platforms while maintaining clear platform boundaries.
  • Understanding of ontology-driven platforms, workflow orchestration, and enterprise AI enablement strategies.

Preferred Experience

  • Palantir Foundry and AIP
  • Databricks, Snowflake, AWS
  • Enterprise AI/ML platforms and data ecosystems
  • Event-driven architectures and API-first integrations
  • AI governance, platform strategy, and architecture standards
  • Operational workflows, human-in-the-loop systems, and AI-enabled decision support
  • Industrial, energy, manufacturing, or other complex enterprise environments

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