Hiring: Senior AI Engineer Company: Systems Planning & Analysis Location: United States Job Posted Time: 2026-09-16 16:20:04 Employment Type: Contract / Remote Target Skills & Keywords : Azure, CI/CD, DAST, FedRAMP, Fine-tuning, Foundry, GCP, GitHub, GitLab, IaC, Kubernetes, LLM, MLOps, OAuth2, ONNX, OWASP, PowerShell, Python, RAG, SAST, Terraform, vLLM About the job Experience: •5-7 years in enterprise software or systems engineering, with a strong recent focus on cloud‑scale AI architectures. •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 •7 years in enterprise software or systems engineering, with a strong recent focus on cloud‑scale AI architectures. •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 Required Skills: •AI Engineering & Delivery (primary focus) •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 multi‑agent frameworks (e.g., Semantic Kernel, LangGraph, AutoGen, or similar) and tool‑use 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. Qualifications: •Bachelor’s degree in computer science, Data Science, Cybersecurity, IT, or related field •Demonstrated experience in GCC High or Azure Government environments •Multi‑cloud 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 •Operational 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 •Operational familiarity with NIST AI RMF and its Generative AI Profile (NIST AI 600-1) Compensation: •Flexible work environment (work from home / hybrid options) Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!