Interested in this AI/ML Engineer role at Mobius LLC?
Apply Now →Skills & Technologies
About This Role
Mobius is an award winning, Small Business Administration (SBA) certified Historically Underutilized Business Zone (HUBZone) company and certified Woman\-Owned Small Business (WOSB) providing engineering, analytical, and programmatic expertise to the Federal Government and commercial customers. Our mission is to provide innovative advanced technology solutions to customers facing issues of national and global significance. We strive to be admired for excellent people, fair and honest partnership, innovative problem solving, and exceptional performance.
Come join our team! Mobius is seeking a AI Engineer.
In this role you will get to provide technical support for the design, evaluation, and advancement of AI\-enabled applications, tools, and workflows. This role is intended for a hands\-on technical professional who understands how modern AI systems are built and can help develop practical solutions using large language models, agentic workflows, orchestration frameworks, and supporting software infrastructure. The right candidate is a builder at heart—someone who explores new frameworks, prototypes ideas, and continuously teaches themselves emerging techniques. Curiosity and passion matter greatly in this role. We want someone who follows the fast\-moving AI ecosystem because they genuinely enjoy it, not just because the job requires it. This position is located in Huntsville, AL.
Duties of a AI Engineer may include:
- Support government led teams in the design, evaluation, and practical application of AI\-enabled tools, workflows, and mission\-focused capabilities
- Provide technical insight across the AI engineering lifecycle, including application design, prototyping, integration, test, evaluation, and operational assessment.
- Assess solutions involving large language models, retrieval\-augmented generation, tool use, agentic workflows, orchestration layers, prompt pipelines, and evaluation harnesses, while helping government stakeholders understand technical tradeoffs, risks, and opportunities
- Review architectures, development approaches, model usage patterns, data flows, and integration strategies to ensure solutions are reliable, observable, testable, secure, and aligned with mission needs
- Evaluate emerging AI frameworks, agentic libraries, and experimentation tools; contribute to prototypes, technical guidance, engineering recommendations, and briefings; and support performance assessments, qualitative reviews, and workflow validation activities.
- Collaborate with platform engineers, product leads, mission users, and government leadership to keep AI efforts technically grounded, operationally relevant, and responsive to the rapidly evolving AI ecosystem
Qualifications
- 12\+ years of relevant experience
- Experience designing, building, integrating, or evaluating AI/ML\-enabled applications or software tools.
- Familiarity with modern AI application patterns, including LLM\-based applications, RAG, prompt engineering, agentic systems, and AI\-assisted workflows.
- Experience with software development fundamentals, APIs, data handling, and system integration.
- Ability to assess technical tradeoffs and communicate them clearly to both engineers and government stakeholders.
- Strong written and verbal communication skills.
- Ability to obtain a TS SCI clearance required
- Experience with Python, JavaScript/TypeScript, or similar languages commonly used in AI application development.
- Familiarity with vector databases, embeddings, model APIs, evaluation frameworks, and agent testing harnesses.
- Exposure to model hosting, inference pipelines, or cloud\-based AI development environments.
- Understanding of secure AI development practices, data protection, and operational constraints in government settings.
- Experience supporting defense, cyber, intelligence, or mission application development.
- Familiarity with human\-in\-the\-loop workflows, AI assurance, and evaluation methodologies.
- Working knowledge of one or more agentic or orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, Haystack, LlamaIndex, or similar ecosystems.
*Education*
- Master's degree in Computer Science, Engineering, Data Science, Mathematics, or related field
*Clearance*
- Active Secret clearance required
Mobius Benefits: Mobius offers a stable work environment, a competitive salary, and a comprehensive benefits package, which includes medical, dental and vision plans, 401k Plan, Flexible Work Schedules, Tuition Reimbursement, Paid Leave and much more.
*Mobius is committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity Employer/Affirmative Action Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class.*
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 Mobius 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.
Mobius LLC AI Hiring
Mobius LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Huntsville, AL, 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.