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About This Role
Location: McLean, VA (On\-site)
Job Type: Full\-Time
Clearance Requirement: Active TS/SCI with CI Polygraph (Required)
Position Overview:
Praescient Analytics is seeking a talented and driven AI Process Integration Engineer to support a critical national security mission with the National Counter Terrorism Center (NCTC).
The AI Process Integration Engineer, Senior bridges the gap between approved, network\-ready AI/ML tools and their operational use across NCTC intelligence analysis, targeting, and screening workflows. Rather than waiting for new tools, this role maximizes the mission value of existing software by redesigning workflows, configuring tools for specific use cases, and driving rapid analyst adoption.
Key Responsibilities:
AI Tool Configuration \& Workflow Integration
- Optimize Existing Tools: Evaluate approved AI/ML tools on the customer network to bridge configuration gaps and maximize underutilized capabilities.
- Mission Customization: Configure approved tools into existing workflows for specific analyst tasks (e.g., entity triage, pattern correlation, RFI processing) without triggering new review board actions.
- Process Redesign: Map current analytic workflows to eliminate manual bottlenecks, designing future\-state processes that embed AI at high\-friction points.
- Standard Operating Procedures: Maintain before/after process documentation, SOPs, and metrics tied directly to mission outcomes.
Tool Enablement \& Change Management
- Prompt Engineering: Build mission\-specific prompt libraries, template guides, and a Document Support Playbook Suite (draft assist, tradecraft review, classification checks) to make AI tools instantly usable for non\-technical analysts.
- Drive Adoption: Work alongside analysts, targeters, and mission leads to deliver hands\-on demonstrations, training materials, and embedded transition support.
- Performance Metrics: Establish KPIs tracking AI utilization, analyst productivity, and cycle\-time reductions to provide leadership with data\-driven evidence for scaling tools.
- Operational Liaison: Serve as the functional bridge between technical AI/ML engineers, the analytic workforce, and program leadership.
Job Requirements \& Qualifications:
- Experience: 10\+ years in AI/ML tool deployment, systems integration, or process engineering—including 5\+ years supporting the IC, DoD, or Federal law enforcement.
- Education: Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field.
- Technical Proficiency: Hands\-on experience with AI/ML tool configuration, prompt engineering, workflow modeling (BPMN), and data pipelines within multi\-classification networks.
- Methodologies: Practical knowledge of Lean Six Sigma, Agile, or continuous improvement frameworks applied to intelligence environments.
- Core Competencies: Strong analytical thinking with the ability to translate complex AI capabilities into practical mission value for non\-technical users.
- Active TS/SCI with CI Polygraph (Required)
What You Can Expect From Us:
- Real opportunity for career growth in an environment where your achievements will be celebrated
- Constant collaboration with numerous teams to ensure client success
- A team that respects and embraces your ideas and expertise
- Coworkers that are motivated by pursuing excellence, rather than the prospect of personal gain
- A workplace dedicated to supporting and bettering public safety and government agencies
Benefits:
- Very competitive salary based on qualifications and experience
- Comprehensive, Company paid healthcare for you (We pay your premiums and deductibles)
- 401(k) with company match
- Travel \& performance incentives
- 3 weeks paid time off (plus Federal Holidays)
- $5K annual training allowance
Praescient Analytics is a Certified Woman\-Owned Small Business (WOSB) with over a decade of expertise in advanced analytics, engineering, and DevOps, specializing in transforming complex data into actionable intelligence for informed decision\-making. Since 2011, we have supported over 40 organizations across diverse domains, including military intelligence operations, financial and fraud investigations, and insider threat detection.
Our team of experts—skilled in cloud computing, artificial intelligence, machine learning, data science, DevOps, and engineering—brings deep experience in solving complex challenges. With a proven track record in federal contracting, we deliver tailored, high\-impact solutions designed to enhance operational efficiency, ensure mission success, and address the evolving needs of our clients. Praescient's innovative and adaptive approach makes us a trusted partner in delivering data\-driven insights and technological excellence for critical missions.
Applicants selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information.
US Citizenship Required
Interested Candidates: Please forward your resume to [email protected] and please visit our website to apply online at www.praescientanalytics.applicantstack.com/x/openings.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Praescient Analytics, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Praescient Analytics AI Hiring
Praescient Analytics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in McLean, VA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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