In this role, you will lead development of systems built on foundation models of all sizes - from small language models (SLMs) suited to edge and cost-constrained deployments, to large language and multimodal models - including custom enterprise copilots, and autonomous agentic workflows. You will ensure these capabilities are deployed securely across local, hybrid, and cloud model backends - spanning Microsoft Azure (including GCC High), Azure Government, Google Cloud Platform (GCP), and on-premises/edge infrastructure.

This is a bridge role: your primary depth is in AI engineering and delivery, with strong working fluency in cloud security and DevSecOps practices. You will partner with - not replace - Infrastructure and Security teams to deliver secure, mission-aligned AI at scale in highly regulated environments

• Build and deploy production AI applications using Azure AI Foundry, Azure OpenAI Service, and Copilot Studio, accounting for service availability differences between Azure Commercial, Azure Government, and GCC High environments.
• Select and right-size models for mission requirements - balancing capability, cost, latency, and deployment constraints across small, medium, and large foundation models (e.g., SLMs such as Phi, frontier LLMs, embedding and multimodal models).
• Engineer agentic AI systems, including multi0agent frameworks (e.g., Semantic Kernel, LangGraph, AutoGen, or similar) and tool0use pipelines, including Model Context Protocol (MCP) - based integrations.
• Develop RAG architectures using Azure AI Search and vector stores, including embedding pipelines, document chunking strategies, and grounding-data governance (Purview/DLP integration).
• Orchestrate model endpoints and optimize inference workloads across local, hybrid, and remote backends - including managed cloud endpoints (Azure AI Foundry/OpenAI), self-hosted inference on AKS, and local/on-prem serving runtimes (e.g., ONNX Runtime, vLLM, Foundry Local, or similar).
• Design backend-agnostic application architectures with abstraction layers that allow models to be swapped or routed between local, hybrid, and cloud endpoints based on data sensitivity, latency, cost, and connectivity constraints.
• Implement MLOps/LLMOps practices: model evaluation harnesses, AI red-teaming (e.g., PyRIT), prompt versioning, and telemetry/observability for AI applications.

• Ensure AI workloads conform to GCC High and Azure Government constraints, including CUI handling, data residency, customer-managed key requirements, and appropriate placement of inference (local vs. cloud) based on data classification.
• Support secure multi0cloud operations across Azure and GCP, partnering with Infrastructure teams.
• Configure AI security guardrails, content safety controls, DLP policies, gateway policies, and alignment safeguards, informed by the NIST AI Risk Management Framework (AI 100-1, AI 600-1) and OWASP Top 10 for LLM Applications.

• Implement AI traffic governance and secure inspection using modern AI gateways.
• Maintain secure inter0cloud connectivity and workload visibility using NSGs, firewall rules, traffic mirroring/network visibility tooling, and service-to-service authentication (OAuth 2.0 client credentials, Entra managed identities, workload identity federation).
• Embed automated security validation (SAST/DAST) into CI/CD pipelines.

• U.S. citizenship.
• Bachelor0s degree in computer science, Data Science, Cybersecurity, IT, or related field
• 5-7 years in enterprise software or systems engineering, with a strong recent focus on cloud0scale AI architectures. [moved from end of list]
• 3-5 years building AI/ML solutions, including 1-2 years hands-on with Azure OpenAI, Azure AI Foundry, Copilot Studio, or equivalent foundation-model platforms
• Experience working across model scales and deployment models - small/specialized through large foundation models, deployed via managed cloud endpoints, self-hosted, or local runtimes - and selecting appropriately for the use case
• Experience developing agentic AI systems and integrating API0driven tools
• Demonstrated experience in GCC High or Azure Government environments
• Multi0cloud security experience spanning Azure and GCP (CSPM/CNAPP, NSGs, traffic mirroring, GCP equivalents)
• Strong CI/CD engineering background with integrated SAST/DAST validation, plus scripting and IaC proficiency (Python, PowerShell, Terraform)
• Expertise in API security, service-to-service/workload identity authentication, and AI gateway architecture
• Familiarity with modern software delivery platforms, including GitHub, GitHub Copilot, and GitLab
• One or more current Microsoft certifications required (e.g., AZ-500 Azure Security Engineer, AI-102 Azure AI Engineer, SC-100 Cybersecurity Architect, or equivalent); GCP security certifications are a plus

• Experience supporting highly regulated environments and compliance frameworks (NIST SP 800053, 800171, CMMC Level 2, FedRAMP)
• Familiarity with NIST AI RMF and its Generative AI Profile (NIST AI 600-1)
• Experience with model fine-tuning, distillation, or quantization for deploying models in constrained, disconnected, or edge environments
• Experience with Kubernetes (AKS) for AI/inference workloads
• Experience with agent-to-agent (A2A) protocols and emerging agent interoperability standards
• Familiarity with hybrid cloud management for AI workloads (e.g., Azure Arc, Azure Local, GPU infrastructure on premises) and DDIL/disconnected operation patterns