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About This Role
Minimum Clearance Required: TS/SCI Responsibilities:
I2X Technologies is a reputable technology services company to the Federal Government. Whether the focus is on space exploration, national security, cyber security, or cutting\-edge engineering applications, I2X is ready to offer you the chance to make a real\-world impact in your field and for your country. We provide long\-term growth and development. Headquartered in Colorado, I2X is engaged in programs across the country and in more than 20 states. Our programs support multiple Federal agencies, the Department of Defense and often focused on the space initiatives of our government customers. Job Description: Journeyman LLM \+ Ontology Software Engineer I2X Technologies is seeking a Journeyman Journeyman LLM Engineer: Job Summary: We are looking for a Journeyman LLM Engineer to develop and enhance intelligent applications that combine Large Language Models (LLMs) with formal ontologies and knowledge graphs.### Responsibilities:
- Design and implement solutions that integrate LLMs with ontologies for improved accuracy and reasoning
- Develop Retrieval\-Augmented Generation (RAG) systems using ontology\-based knowledge
- Build and maintain knowledge graphs and ontologies to support LLM applications
- Fine\-tune, prompt engineer, and evaluate LLMs for specific business use cases
- Create APIs and services that allow applications to interact with LLMs and ontology data
- Ensure the reliability, scalability, and ethical use of LLM\-powered features
- Collaborate with data scientists, engineers, and domain experts
Qualifications:
Requirements:* 3–6 years of experience in software engineering with a focus on AI/ML or LLM development
- Solid experience and proficiency with Java for building scalable back\-end services and integrating with LLMs
- Strong hands\-on experience working with Large Language Models
- Practical experience with ontologies, knowledge graphs (RDF, OWL, or similar), and semantic technologies
- Proficiency in Python and relevant frameworks (LangChain, LlamaIndex, etc.)
- Experience with vector databases and retrieval systems
- Solid understanding of software engineering best practices and version control (Git)
Preferred or Desirable to have:* Experience in ontology modeling or semantic web technologies
- Knowledge of graph databases (Neo4j, GraphDB, etc.)
- Familiarity with evaluation frameworks for LLMs
- Experience with cloud platforms (AWS, Azure, or GCP)
Essential Requirements:
US Citizenship is required.
Clearance: TS/SCI
In compliance with Colorado’s Equal Pay for Equal Work Act, the annual base salary range for this position is listed . Please note that the salary information is a general guideline only. I2X Technologies considers factors such as (but not limited to) scope and responsibilities of the position, candidate’s work experience, education/training, key skills, internal peer equity, as well as, market and business considerations when extending an offer. Physical Demands:
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job with or without reasonable accommodation.
While performing the duties of this job, the employee will regularly sit, walk, stand and climb stairs and steps. May require walking long distance from parking to work station. Occasionally, movement that requires twisting at the neck and/or trunk more than the average person, squatting/ stooping/kneeling, reaching above the head, and forward motion will be required. The employee will continuously be required to repeat the same hand, arm, or finger motion many times. Manual and finger dexterity are essential to this position. Specific vision abilities required by this job include close, distance, depth perception and telling differences among colors. The employee must be able to communicate through speech with clients and public. Hearing requirements include conversation in both quiet and noisy environments. Lifting may require floor to waist, waist to shoulder, or shoulder to overhead movement of up to 20 pounds. This position demands tolerance for various levels of mental stress.
I2X Technologies is an Engineering and Information Technology Company focused on providing Services to the Federal and State Government. I2X offers a competitive compensation program and comprehensive benefits package to our employees.
Salary Context
This $92K-$105K range is in the lower quartile 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 Isys Technologies, 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 ($98K) sits 55% below the category median. Disclosed range: $92K to $105K.
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.
Isys Technologies AI Hiring
Isys Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Omaha, NE, US. Compensation range: $105K - $105K.
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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