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About This Role
Job Role: Senior Solutions Architect
City / State / Region: Washington, D.C. Metro Area
Location: Pentagon SCIF (Onsite – 100%)
Travel: Minimal (\<10%)
Job Type: Full\-Time
Clearance: Active Top Secret (TS) with ability to obtain SCI
Job Posting Estimated Close Date: July 30, 2026
### Overview:
INTECON is seeking a Senior Data Science Consultant to support the U.S. Air Force A33B Mission Assurance Construct Support (MACS) program by leading advanced analytics initiatives, developing AI/ML\-enabled decision support capabilities, and providing expert consultation on data science applications for Mission Assurance within a Pentagon SCIF environment.
### Key Responsibilities:
- Lead design, development, and implementation of advanced analytics and data science solutions within the Palantir Foundry platform
- Develop and deploy AI/ML models and algorithms to support Mission Assurance risk analysis, trend identification, and predictive analytics
- Evaluate and recommend automation and AI/ML techniques for course of action (COA) analysis and risk assessment
- Build and maintain analytical applications, dashboards, and decision support tools for senior Air Force leadership
- Provide expert consultation to Government stakeholders on data science best practices, methodologies, and emerging capabilities
- Develop data pipelines and analytical workflows supporting mission analysis, risk management, and assessment processes
- Conduct advanced statistical analysis and modeling to identify patterns, anomalies, and insights from Mission Assurance data
- Collaborate with mission analysts, data managers, and solutions architects to integrate data science capabilities into operational workflows
- Document analytical methodologies, model performance metrics, and validation approaches
- Mentor and provide technical guidance to junior data analysts and technical staff
- Support continuous improvement of analytical capabilities and identification of new data science opportunities
- Present complex analytical findings and recommendations to senior leadership in clear, actionable formats
### Clearance Requirements:
- Active Top Secret (TS) security clearance with the ability to obtain and maintain TS/SCI with caveats. U.S. Citizenship required.
### Qualifications:
- Master's degree (M.A. or M.S.) from an accredited institution in Data Science, Computer Science, Statistics, Mathematics, or related quantitative field
- Over 10 years of experience in data science, advanced analytics, or related technical roles
- Palantir Foundry Application Developer Certification (REQUIRED)
- Demonstrated experience developing and deploying AI/ML models in operational environments
- Proficiency with Python, R, or similar data science programming languages
- Experience with machine learning frameworks (TensorFlow, PyTorch, scikit\-learn, or similar)
- Strong statistical analysis and modeling skills
- Excellent written and verbal communication skills with ability to translate complex technical concepts for senior leadership
### Preferred Qualifications:
- Experience supporting DoD or Air Force analytical programs or decision support systems
- Familiarity with Mission Assurance, risk management, or critical infrastructure analysis domains
- Experience with natural language processing (NLP) and text analytics
- Knowledge of DoD data standards and interoperability requirements
- Prior experience briefing O\-6/GS\-15 level and above
- Experience with cloud\-based analytics platforms and distributed computing
- Certifications in data science or machine learning (e.g., AWS Machine Learning Specialty, Google Professional ML Engineer)
### Why Join INTECON?
At INTECON, we are at the forefront of defense, security, and technology, driving innovation, collaboration, and strategic excellence. Our employees are the foundation of our success, and we are committed to empowering them with the resources, opportunities, and support they need to thrive. As part of our team, you will play a critical role in supporting high\-level defense leadership and national security operations, making a tangible impact in a fast\-paced, mission\-driven environment. Join us in shaping the future of defense and security.
### Benefits:
- Comprehensive Group Health Plans (Medical, Dental, and Vision) coverage
- Company\-paid Short\-Term and Long\-Term Disability, Life, and AD\&D Insurance
- Critical Illness and Accident Insurance
- Flexible Spending Accounts and Supplemental Plans Available
- Generous Paid Time Off and Holiday Pay
- 401k Retirement Plan with Company Match
- Company\-paid Training/Development Programs, and Educational Assistance Program
- Employee Assistance, Health Advocacy, and Financial Wellbeing Programs
INTECON is proud to be an Equal Opportunity Employer committed to fostering diversity and inclusiveness. We firmly uphold the principle of Equal Pay for Equal Work, without regard to race, color, creed, religion, national origin, sex, sexual orientation, gender identity and expression, age, disability, eligible veteran status, or any other protected characteristic.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Intecon, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Intecon AI Hiring
Intecon has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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