> Markdown version of [/jobs/ext/4602695-sr-ai-platform-architect](https://www.wearedevelopers.com/jobs/ext/4602695-sr-ai-platform-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Sr. AI Platform Architect

- **Company:** Scion Staffing, Inc.
- **Location:** North Reading, MA, United States
- **Experience:** Expert
- **Salary:** $220,000.0
- **Contract:** Contract
- **Skills:** Artificial Intelligence, ARM Architecture, Microsoft Azure, Information Systems, Continuous Integration, Data Architecture, Google Cloud, Enterprise Software Applications, Office365, Large Language Models, Snowflake, IT Architecture, Model Validation, Generative AI, Agentic-AI, Microsoft Copilot Studio, AI Platforms, Information Technology, Machine Learning Operations, Azure AI
- **Published:** November 6, 2026
- **Apply:** https://scionstaffing.com/apply-with-scion-staffing/

## About the Role

* Bachelor's degree in Computer Science, Information Systems, or related field.
 * 6+ years of IT experience across enterprise architecture, AI/ML, and/or generative AI.
 * Experience architecting and implementing enterprise-scale AI solutions.
 * Strong understanding of GenAI, LLMs, Agentic AI, MLOps, and LLMOps.
 * Strong Azure experience and working knowledge of Google Cloud.
 * Experience with Microsoft 365 Copilot, Copilot Studio, Azure AI, Vertex AI, and Snowflake/Cortex AI.
 * Knowledge of data architecture, integration, governance, security, MCP, CI/CD, and Infrastructure as Code.

## Description

Join a high-impact AI transformation initiative where you'll help define how AI is architected, governed, and scaled across the enterprise. This role combines enterprise architecture, AI engineering, data, security, and organizational enablement, giving you the opportunity to influence both technology strategy and adoption., * Define AI Architecture: Translate AI strategy into scalable architecture, roadmaps, integration patterns, and reusable standards across AI, data, applications, and enterprise platforms.

 * Establish AI Engineering Standards: Develop best practices for MLOps, LLMOps, agentic AI, model/agent lifecycle management, deployment, monitoring, and evaluation.
 * Lead AI Platform Integration: Guide integration across cloud AI, data, and enterprise SaaS platforms and lead the architecture for a secure, governed MCP capability connecting AI agents with enterprise systems and data.
 * Evaluate Emerging Technology: Assess new AI platforms, models, frameworks, and methodologies and recommend solutions based on value, scalability, etc.
 * Drive AI Adoption: Partner with business and technology leaders to identify high-value use cases and move AI initiatives from experimentation into production.
 * Establish Governance: Partner with security and risk leaders on AI governance, data privacy, model risk, responsible AI, compliance, and security.
 * Optimize for Scale: Ensure AI solutions are reliable, scalable, cost-effective, and operationally ready.