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About This Role
- Requisition ID: 3002
- Standard Title: Principal Systems Engineer
- Required Security Clearance: Top Secret/SCI with Full Scope Polygraph
- Location: Annapolis Junction, MD
- Work Type: On\-Site
- Shift: First
- Referral Eligibility: Eligible
- U.S. Citizenship Required? Yes
Position Summary
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Base\-2 Solutions is seeking a Systems Engineer 6 to apply senior systems engineering expertise across the full system life cycle, ensuring the technical integrity, quality, and completeness of work products and deliverables. This role supports AI\-focused cloud and platform engineering efforts, with emphasis on AWS, AI/ML systems, security, and data lifecycle management.
Essential Duties and Responsibilities
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- Apply systems engineering principles throughout the system life cycle phases of concept, development, production, utilization, support, and retirement.
- Interface with the Government regarding systems engineering technical considerations and associated problems, issues, or conflicts.
- Communicate with program personnel, government overseers, and senior executives.
- Maintain responsibility for the technical integrity, quality, and completeness of work performed and deliverables associated with one or more of the 25 ISO/IEC 15288 process areas.
- Support technical process areas including stakeholder requirements definition, requirements analysis, architectural design, implementation, integration, verification, transition, validation, operation, maintenance, and disposal.
- Support project process areas including project planning, project assessment and control, decision management, risk management, configuration management, information management, and measurement.
- Support enterprise process areas including project portfolio management, infrastructure management, lifecycle model management, human resource management, and quality management.
- Support agreement process areas including acquisition and supply.
Required Qualifications
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- Active TS/SCI with Full Scope Polygraph.
- Experience in systems engineering sufficient to meet one of the stated education/experience pathways.
- Experience applying systems engineering principles across the full system life cycle.
- Ability to ensure technical integrity, quality, and completeness of systems engineering deliverables.
- Ability to communicate effectively with Government stakeholders, program personnel, and senior executives.
- Experience with cloud architecture, cloud engineering, or platform engineering, especially in AWS.
- Experience with AI/ML system design and implementation.
- High\-level understanding of the AI lifecycle, including development, training, inference, and monitoring.
- Experience with ML pipeline development, model deployment, and DevOps/MLOps.
- Experience with system security and networking, particularly implementation of system security on classified systems.
- Experience with data engineering and data lifecycle management.
- Excellent communication and collaboration skills with varied stakeholder groups and the ability to proactively engage them to understand requirements and inform system architecture decisions.
Preferred Qualifications
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- Experience with Amazon SageMaker.
Required Education and Experience Equivalency
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Education Years of Experience
High School Diploma/GED 24
Associates Degree 24
Bachelors' Degree 20
Masters' Degree 18
PhD 18
Required Certifications
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- None specified.
Required Security Clearance
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- Active TS/SCI with Full Scope Polygraph.
Pay \& Benefit Highlights
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Compensation
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- Competitive fixed salary or hourly pay (based on experience, skills, location, and internal equity).
- Employee referral bonuses up to $10,000 per hired referral.
- Additional bonus opportunities for exceptional performance and contributions to business development and company growth (role\-dependent).
Health
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- 100% company\-paid medical premiums for employees and eligible dependents.
- Choose from multiple plan options with CareFirst, Kaiser, and UnitedHealthcare, including PPO, POS, HMO, and HSA\-compatible plans.
- 100% company\-paid dental premiums for employees and eligible dependents.
- 100% company\-paid vision premiums for employees and eligible dependents.
Income Protection
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- 100% company\-paid premiums for short\-term disability.
- 100% company\-paid premiums for long\-term disability.
- 100% company\-paid premiums for accidental death \& dismemberment (AD\&D).
- 100% company\-paid premiums for life insurance up to $200,000\.
Retirement
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- 401(k) with immediate vesting: 4% company match plus a 4% non\-elective company contribution (8% total).
- 401(k) pre\-tax and Roth options.
Leave
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- Up to 20 days of flexible paid time off (PTO).
- 11 paid floating holidays.
Work\-Life Balance
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- Flexible work schedules, including flex time and compressed work periods (contract and project\-dependent).
\#LI\-PRO
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 Base-2 Solutions, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Base-2 Solutions, LLC AI Hiring
Base-2 Solutions, LLC has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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