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About This Role
Job Type: Full\- Time
Workplace Type: Onsite
Clearance: Secret with ability to get a DHS Suitability
Must be a U.S. Citizen
Job Summary
Castalia is seeking a AI/ML Engineer to support this critical customer mission. The AI/ML Engineer will develop and implement automation solutions to support machine learning technology integration activities, focusing on agentic workflow development, automated analysis workflows, and integration automation. This role addresses manual workflow pain points and enables scaling of technology capabilities through intelligent automation.
Roles and Responsibilities
A qualified candidate will perform the following duties and responsibilities, but are not limited to:
- Develop automated data pipelines for machine learning technology integration initiatives
- Create automation workflows for malware sample collection, detonation, and taxonomy
- Implement AI/LLM integrations with Databricks and other cloud services
- Implement data integration automation between pilot and production systems
- Support Automated Malware Analysis (AMA) platform development and enhancement
- Develop scripts and tools to automate repetitive technical tasks
- Create data transformation and normalization automation
- Support AI/ML data preparation and feature engineering automation
- Develop monitoring and alerting automation for integrated systems
- Implement API integrations for data sharing and workflow automation
- Document automation solutions and maintain code repositories
- Support scaling automation from pilot to production environments
- Optimize automation performance and efficiency
- Collaborate with analysts to identify automation opportunities
- Ensure automation solutions maintain data quality and integrity
- Support continuous improvement of automated capabilities
Required Qualifications
- BS in Computer Science, Data Science, or related degree, or HS Diploma and 7\+ years of directly relevant experience.
- 5\+ years of experience in automation engineering, data engineering, or software development
- Experience with data ingestion pipeline design on AWS
- Experience with AI/LLM configuration, tuning, and integration
- Experience with Machine Learning (ML) pipelines
- Strong programming skills in Python, with experience in data manipulation libraries
- Experience Amazon Bedrock (model access, endpoints, API Integration)
- Knowledge of automation frameworks and orchestration tools
- Experience with APIs and integration technologies
- Understanding of database systems and data modeling
- Familiarity with version control (Git) and CI/CD practices
- Strong problem\-solving and analytical skills
- Ability to translate business requirements into automated solutions
- Strong analytical and problem\-solving skills
- Excellent documentation and communication abilities
Preferred Qualifications
- TS/SCI clearance
- Experience with malware analysis automation or cybersecurity tools
- Knowledge of AI/ML pipelines and data preparation
- Experience with Databricks and Databricks AI
- Experience with workflow orchestration tools (Airflow, Prefect)
- Background in cybersecurity or threat intelligence automation
- Experience with containerization and orchestration (Docker, Kubernetes)
- Knowledge of streaming data platforms (Kafka, Kinesis)
- Familiarity with big data technologies (Spark, Hadoop)
- Experience supporting federal cybersecurity programs
- DoD 8140 IAT Level III
- CSSLP
Physical Requirements/Work Environment
- Normal office environment.
Travel
- As required
Company Description
Castalia Systems is a proven business partner providing mission critical solutions to the Federal Government. We provide cutting edge solutions from Securing and Managing Data to Systems Engineering and Development. Castalia Systems is a pioneer in Artificial Intelligence Design and Application.
With our vast knowledge of our customers’ needs and relevant technology, our team is able to bring successful solutions to every mission. We are one\-upping our competitors by providing premium IT solutions and platforms with cutting\-edge technology so it’s so evident when you compare us with anyone.
Compensation
At Castalia Systems, we provide you with opportunities and choices and support your total well\-being. Our benefits include: Medical, dental, vision coverage, 401k matching, generous PTO, paid holidays, professional training opportunities, and even pet insurance to ensure your furry friends are cared for too. All regularly scheduled employees working at least 30 hours per week are eligible to participate in Castalia Systems’ benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits.
Salary at Castalia Systems is determined by various factors, including but not limited to location, position knowledge, skills, competencies, and experience, as well as contract\-specific affordability and organizational requirements. The projected compensation range for this position is $135,000\.00 to $145,000\.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Castalia Systems’ total compensation package for employees.
Disclaimer
Castalia Systems is an equal employment opportunity and affirmative action employer and strives to comply with all applicable laws prohibiting discrimination based on race, color, creed, sex, sexual orientation, age, national origin, or ancestry, physical or mental disability, veteran status, marital status, HIV\-positive status, as well as any other category protected by federal, state, or local laws. All such discrimination is unlawful, and all persons involved in the operations of the company are prohibited from engaging in this type of conduct.
Salary Context
This $135K-$145K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Castalia Systems, 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. This role's midpoint ($140K) sits 36% below the category median. Disclosed range: $135K to $145K.
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.
Castalia Systems AI Hiring
Castalia Systems has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Arlington, VA, US. Compensation range: $145K - $145K.
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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