***This is strictly a W2 position.
***Candidates MUST BE local to DC, Maryland, Virginia, or West Virginia only!
Terms of Employment
Contract Duration: W2 Contract, 6-Month (potential for extension)
Location: 100% Remote.
DMV area and WV candidates are highly preferred but not required
Overview
Work with a forward-thinking enterprise AI Center of Enablement to design, build, and operationalize next-generation AI infrastructure. As an AI Platform Engineer, you will transition conceptual reference architectures into secure, scalable, and reusable components that will power AI initiatives across the entire organization. This role acts as the foundational engineering engine driving the safe deployment of agentic AI capabilities within an evolving multi-cloud environment.
Key Responsibilities
Implement enterprise AI platform capabilities and technical reference architectures within a cloud infrastructure.
Build and scale reusable AI platform services, shared software components, and robust integrations.
Develop and support agentic AI reference architectures, utilizing agents with Model Context Protocol (MCP) A to A integrations.
Partner closely with enterprise architecture, cloud engineering, security, governance, and operations teams to safely operationalize AI workflows.
Troubleshoot complex integrations and build out telemetry, monitoring, and event-driven architectures.
Required Qualifications
5+ years of software engineering experience coupled with a strong engineering mindset and industry best practices.
5+ years of experience in cloud platform engineering, with a minimum of 2 years specifically focused on Azure (or an equivalent cloud architecture like AWS with demonstrated ability to transfer cloud concepts instantly).
1+ years of substantial, hands-on experience building and deploying AI platform capabilities, with concrete projects to show mastery.
Strong programming proficiency with Python.
Direct experience developing event-driven architectures, message-based integrations, and configuring monitoring tools such as Splunk.
Strong soft skills including the ability to deal with ambiguity, manage quick turnarounds, and maintain excellent cross-team communication.
Preferred Qualifications
Hands-on experience working directly with agentic AI models and workflows.
Experience implementing Model Context Protocol (MCP) configurations.
Multi-cloud familiarity spanning both Azure and AWS platforms.
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