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About This Role
Company Overview:
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.
Work Location and On-Site/Telework Requirements: Hybrid – NIH, Bethesda, MD. On site for discovery workshops (typically 1–2 days/week during the first 120 days, then as scheduled).
Citizenship: U.S. Citizenship required
Clearance: 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.
Salary Range: $150,000-$160,000 yearly salary
Overview:
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.
Responsibilities:
- 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.
Tools & Technology Environment: Python, scikit-learn, PyTorch; UEBA and analytics in Splunk/Microsoft Sentinel; identity risk signals (e.g., Microsoft Entra ID Protection); EDR/MDM telemetry (Defender, CrowdStrike); SOAR platforms; Azure AI/AWS SageMaker or Bedrock; policy-as-code (e.g., OPA/Rego).
Required Qualifications:
- Bachelor's degree in computer science, data science, or a related field plus 8+ years of experience, including AI/ML use-case design.
- Experience designing risk or trust-scoring models on security telemetry (identity, endpoint, network, or SIEM data).
- Working knowledge of the NIST AI RMF 1.0 and federal AI governance expectations.
- Hands-on Python and ML framework experience.
- Ability to obtain an NIH suitability determination and PIV credential; fluent in English.
Preferred Qualifications:
- Master's degree; experience with the NIST AI RMF Generative AI Profile and current OMB AI memoranda.
- Familiarity with NIST SP 800-207, MITRE ATLAS, and the OWASP Top 10 for LLM Applications.
- Experience with PHI/PII or health research data governance.
- Security certification (Security+, CISSP) or cloud AI certification.
- Current National Institutes of Health (NIH) or U.S. Department of Health and Human Services (HHS) experience is highly preferred.
Commitment to Diversity -
eTelligent Group provides equal employment opportunities (EEO) to all applicants without regard to race, color, religion, gender, sexual orientation, gender identity, nations origin, age, disability, genetic information, marital status, amnesty, status as a covered veteran, and any other characteristic provided in accordance with applicable, federal, state and local laws.
Salary Context
This $150K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At eTelligent Group LLC, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($155K) sits 27% below the category median. Disclosed range: $150K to $160K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
eTelligent Group LLC AI Hiring
eTelligent Group LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bethesda, MD, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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