AI​/ML Solutions Architect

Over the past 15 years, eTel has delivered essential solutions for the federal government by securing and managing data, providing scalable identity access, modernizing legacy systems, and building high-performance platforms. By integrating new technologies and ensuring reliable operations we help agencies stay prepared for future challenges As a premier technology solutions and services company to the US federal government, eTel possesses longstanding relationships across the federal civilian marketplace.

Other customers include the broader Treasury Department, Commerce Department, and State Department.

eTel offers integrated CMMI Level 3 processes, tools, and techniques with innovative, cost-efficient, and secure solutions to address complex challenges. eTel also holds ISO 9001:2015, ISO/IEC 27001:2013, and ISO/IEC 20000-1:2018 certifications, and offers dedicated subject matter experts (SMEs) and thought leaders that possess a deep understanding of customers’ environments and challenges.

Hybrid - NIH, Bethesda, MD. On site for discovery workshops (typically 1-2 days/week during the first 120 days, then as scheduled).

All staff must obtain NIH suitability and a PIV credential and be fluent in English. Anyone doing risk or vulnerability testing needs a current T2 (BI) or higher investigation.

$150,000-$160,000 yearly salary

You will lead Task 3, AI-Enabled Zero Trust Use Case Identification and Development, under the NIH Governance, Risk & Compliance (GRC) Zero Trust Architecture (ZTA) Support Services task order for the NIH Office of the Chief Information Officer (OCIO). Working in the Architecture Pod, you will find where AI can improve Zero Trust policy decisions, design how those signals feed enforcement, and measure whether they are worth deploying.

All of this work must fit NIH's AI governance and data-governance requirements.

• Lead a six-week discovery with ISAO, GRC, OITA, and CIT to build the AI Use Case Inventory across all ZTA pillars (Subtask 3.1, due at 120 days).
• Score each use case on risk reduction, feasibility with NIH's current telemetry, mission impact, and data-governance readiness, then tier it as deploy now, design next, or watch.
• Record for each use case its NIST AI RMF 1.0 function (Govern, Map, Measure, Manage), data classification, human-in-the-loop requirement, and OMB AI use-case inventory disposition.
• Produce the AI technical architectures (Subtask 3.2): data-flow diagrams; model requirements (inputs, features, retraining cadence, drift thresholds); trust-scoring logic written as policy-as-code that the Policy Engine can consume; and integration patterns for identity-provider risk APIs, endpoint compliance evaluation, network policy controllers, and SOAR playbooks.
• Design use cases such as continuous authentication risk scoring, credential-misuse detection, device risk classification from EDR/MDM telemetry, API and workload-identity anomaly detection, ML classification of PHI/PII/research data, UEBA for insider risk, and AI-assisted SOAR triage.
• Extend Zero Trust to AI agents and copilots: authorization of individual agent actions and tool calls, data-leakage controls, and prompt-injection risk.
• Define and run the Subtask 3.3 measurement protocol (detection rate, false-positive rate, time to detect, analyst hours saved, enforcement latency) in controlled evaluations on NIH telemetry. Recommend whether each capability should enforce or stay advisory.

Python, scikit-learn, PyTorch; UEBA and analytics in Splunk/Microsoft Sentinel; identity risk signals (e.g., Microsoft Entra ); EDR/MDM telemetry (Defender, Crowd Strike); SOAR platforms;
Azure…