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About This Role
Who We Are: Oasys International LLC (Oasys) is a growing federal government contractor delivering innovative technology, engineering, and professional services in support of critical missions across the civilian, defense, and homeland security communities. Our continued growth and commitment to excellence have earned Oasys recognition on the Inc. 5000 list of America’s fastest-growing private companies for five consecutive years and as a Best Place to Work in Virginia for the past two years.
At Oasys, our people are at the heart of everything we do. Our team of talented technologists, engineers, consultants, and subject-matter experts works alongside our federal customers to solve complex challenges, modernize critical systems, and make a meaningful impact on the missions we support.
What makes Oasys different is the experience we strive to create for our employees. We offer the opportunity to do meaningful work while being part of a collaborative, people-focused organization where your ideas, expertise, and contributions are valued. We believe great people do their best work when they have opportunities to grow, are recognized for what they bring to the team, and have the flexibility and support needed to build a rewarding career.
Whether you are looking to deepen your technical expertise, take on new challenges, or grow into your next leadership opportunity, Oasys is a place where you can build your career while contributing to missions that matter.
Join Team Oasys and bring your talent to a company where your work is valued, your growth is supported, and your impact matters.
Position Summary:
Oasys is seeking a Senior Data and AI Engineer with a focus on enterprise analytics, business intelligence, data integration, and mission decision support for the Air National Guard Chief Data and Artificial Intelligence Office (CDAO). This senior professional will translate mission and business requirements into reliable data products, reporting solutions, and analytical models using Microsoft Power BI, Power Query, Palantir Foundry or Envision, and approved enterprise platforms. The role will combine data analysis, domain expertise, stakeholder engagement, and technical coordination to improve data quality, readiness reporting, and operational decision-making. The individual will partner with data engineers, application developers, AI specialists, and security teams for advanced platform engineering and AI implementation.
Primary Responsibilities:
- Gather and translate Government mission, financial, manpower, and operational requirements into data integration specifications, reporting requirements, analytical models, and acceptance criteria.
- Develop, maintain, validate, and improve Power BI dashboards, reports, and analytical products that consolidate enterprise data and provide actionable insights to mission stakeholders.
- Use Power Query and approved platform capabilities to prepare, cleanse, standardize, transform, validate, and reconcile data from enterprise sources.
- Support data integration and transformation in Palantir Foundry, Envision, or comparable enterprise platforms; coordinate complex pipeline development and platform changes with engineering specialists.
- Define business rules, data definitions, calculation logic, source mappings, and data-quality checks in collaboration with Government data owners and technical teams.
- Identify discrepancies, investigate root causes, document corrective actions, and track resolution to improve accuracy and consistency of data products.
- Build analytical models, forecasts, performance measures, and executive reporting to support resource allocation, readiness, workforce planning, and operational decisions.
- Coordinate with technical specialists supporting Azure, Azure Databricks, AskSage, Advana, and related platforms to ensure engineering solutions reflect mission requirements.
- Support AI-enabled analytics initiatives by identifying mission use cases, preparing and validating source data, evaluating outputs, and documenting business acceptance criteria with AI specialists.
- Follow applicable CUI handling, access-control, data-governance, and information-security procedures; coordinate implementation of technical security controls with authorized security and engineering personnel.
- Partner with application developers and mission stakeholders on data and reporting requirements for tools such as the ANGRC HR Management Tool, Flying Hour Tool, Commander’s Hub, and ANG Kneeboard.
- Participate in testing, user acceptance, release readiness, and ongoing support; maintain analytical documentation, data definitions, process guides, and change records in authorized repositories.
- Provide demonstrations, user training, mentoring, and executive briefings; communicate findings, limitations, and recommendations to technical and nontechnical audiences.
- Collaborate with Government stakeholders, analysts, engineers, and developers to prioritize improvements and deliver accurate, usable mission data products.
Skills and Qualifications:
- Strong enterprise data analysis, business intelligence, analytical modeling, data preparation, reconciliation, and data-quality skills.
- Hands-on experience developing and maintaining Microsoft Power BI reporting and dashboards, with Power Query or comparable data transformation tools.
- Experience supporting data integration, transformation, or analytics in Palantir Foundry, Envision, or comparable enterprise data platforms.
- Ability to translate complex business and mission requirements into reporting solutions, integration specifications, analytical models, and measurable acceptance criteria.
- Experience analyzing enterprise financial, workforce, operational, or readiness data and presenting findings to senior leaders.
- Working knowledge of data governance, information protection, and Government data-handling procedures, with the ability to coordinate technical controls with designated specialists.
- Strong troubleshooting, documentation, stakeholder engagement, collaboration, training, and team leadership skills.
Required Education and Experience:
- Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Software Engineering, Information Systems, Mathematics, Statistics, Economics, Finance, Financial Management, Business Analytics, or a related technical field.
- Minimum of eight years of relevant experience in enterprise analytics, business intelligence, financial or operational analysis, data integration, data engineering, or related work, including development or improvement of data-driven decision-support capabilities.
- Demonstrated experience delivering analytical products, dashboards, reporting solutions, or data integration and validation activities for complex enterprise or Government organizations.
- Demonstrated experience collaborating with cross-functional stakeholders to define requirements, validate data, communicate analytical findings, and support implementation or user adoption.
- A master’s degree in a related discipline is preferred. An equivalent combination of education, relevant experience, and professional training may be considered where permitted by applicable contract requirements.
Preferred Experience & Certifications:
- Experience supporting DoD, Air Force, Air National Guard, or National Guard Bureau missions and enterprise systems.
- Experience with budgeting, forecasting, resource allocation, flying-hour programs, manpower analytics, or readiness reporting.
- Experience with SQL, Python, Microsoft Azure, Azure Databricks, advanced ETL/ELT, semantic models, Git, CI/CD, or AI/ML integration.
- Experience with AskSage, Advana or Warfighter Data Platform, Power Apps, Power Automate, and applicable IL4/IL5 environments.
- Microsoft Power BI, Fabric, Azure, Databricks, or Palantir credentials or relevant platform training; DoD Financial Management Certification or comparable analytics or professional credentials.
- CompTIA Security+ or another cybersecurity certification where appropriate to assigned duties; any certification mandated by the contract or assigned work role remains required.
Clearance:
- Active SECRET security clearance required.
Work Location:
- Joint Base Andrews, Maryland – Hybrid.
- The position is expected to support on-site performance and may telework up to three business days per week on a static, recurring schedule, subject to Government approval and mission requirements. Classified work must be performed on site.
- Remote work may be authorized only by a Government-approved exception after validation of expert-level capability in data ingestion, cleaning, transformation, serving/deployment, and application/dashboard development in both Palantir Foundry and Microsoft Power BI.
Oasys is proud to be an equal opportunity employer for all protected groups, including protected veterans and individuals with disabilities.
Salary Context
This $110K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Oasys, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($142K) sits 33% below the category median. Disclosed range: $110K to $175K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Oasys AI Hiring
Oasys has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Andrews AFB, MD, US. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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