Senior AI engineer

Job Title: Senior AI engineer Location: Washington DC - Remote Job Type: Contract Job Description Responsibilities 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. Cloud Security & AI Safeguards • 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 multi‑cloud 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. Infrastructure, Networking & CI/CD • Implement AI traffic governance and secure inspection using modern AI gateways. • Maintain secure inter‑cloud 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. Qualifications Required Qualifications • U.S. citizenship. • Bachelor’s 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 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 • 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 API‑driven tools • 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 • 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 Preferred Qualifications • Experience supporting highly regulated environments and compliance frameworks (NIST SP 800‑53, 800‑171, 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