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About This Role
DEPLOY's Client's Cyber Works and Digital Engineering teams in Huntsville, Alabama design, build, and integrate emerging AI/ML technologies to harden and secure the systems that defend the nation, across ground, missile defense, space, and installation infrastructure S\&T programs. We're expanding our Secure AI practice to build and assure trusted, robust AI/ML solutions that interpret complex datasets, predict outcomes, and automate decision\-making in support of critical military platforms.
*This is a consolidated announcement covering multiple tracks; your assignment may emphasize one or a blend of: Classic ML \& Predictive Modeling, LLM/GenAI Applications, AI Assurance \& Responsible AI, Edge AI/ML Deployment, and hardening AI/ML systems against adversarial threats. All tracks require independent research, cross\-functional collaboration, and the ability to clearly communicate complex technical work to stakeholders.*
Core Responsibilities:
- Design, develop, and maintain machine learning models, from classical algorithms (regression, tree ensembles, clustering) to modern deep learning architectures.
- Build LLM\-enabled applications and retrieval\-augmented generation (RAG) pipelines, including vector embedding generation, vector database integration, and prompt/context engineering.
- Develop AI assurance and evaluation tooling: robustness testing, bias/fairness analysis, model traceability, red\-team/adversarial testing, and audit artifact generation.
- Optimize and deploy models for production and edge environments (quantization, compression, containerized inference, ONNX/TensorRT).
- Implement secure model and data pipelines, defend against adversarial ML threats, and ensure supply\-chain integrity (SBOM) for AI components.
- Conduct data processing/analysis to improve model accuracy; document and present development processes and assurance evidence to stakeholders.
- Contribute to Agile, team\-based planning and estimating in a fast\-paced, collaborative environment.
Minimum Requirements:
- Bachelor's degree or equivalent experience in CS/CPE/EE/Data Science (or related field).
- Proven experience in one or more: ML/LLM development, assurance/evaluation tooling, or deploying edge computing solutions.
- Hands\-on experience with ML frameworks such as TensorFlow, PyTorch, or Scikit\-learn.
- Proficiency in Python and at least one additional language (Java, C\+\+).
- Strong understanding of data structures, data modeling, and software architecture.
Highlighted Skills \& Experience:
- Modern AI: LLM frameworks and application development; vector embeddings and vector databases; RAG architectures; prompt/context engineering; LLM fine\-tuning; model evaluation and benchmarking.
- Classic ML: Feature engineering, model selection/tuning, statistical analysis, and predictive modeling across structured and unstructured data.
- AI Assurance: Responsible AI practices (robustness, bias/fairness, traceability); adversarial ML defenses; red\-team testing; auditability and compliance documentation.
- Edge \& Deployment: ONNX/TensorRT, quantization/compression, ARM/NVIDIA Jetson/DSP targets, containerized inference, MLOps in controlled/classified environments.
- Platform \& DevSecOps: REST APIs, CI/CD for software and ML systems, SAST/DAST, Infrastructure\-as\-Code, cloud platforms (AWS, Azure, GCP).
- Domain: Familiarity with computer networking, secure system integration for mission platforms, and test/V\&V support (Python/MATLAB analysis).
- Contributions to open\-source AI/ML projects; experience deploying AI models in production, classified, or embedded environments.
- Understanding of computer security principles and secure software development lifecycle (SSDLC) practices.
Why This Role Matters:
You'll join a high\-impact team solving some of the DoD's hardest problems in AI\-enabled system security, building the next generation of trusted, auditable, and mission\-ready AI for national defense.
About DEPLOY's Client
For the past 43 years, DEPLOY's client has provided industry\-leading technical and engineering solutions in the fields of Defense, Energy, Space, and Environment. As a small, family\-oriented business, DEPLOY"s client provides a compelling benefits package including a generous profit\-sharing plan, competitive salaries, and perhaps most importantly, the opportunity to work alongside talented professionals leveraging cutting\-edge technologies to solve complex and engaging problems.
Why employees love working for DEPLOY's client
DEPLOY"s client is committed to creating a company that is known for its respect and care for employees. We understand that happy employees are what keeps our business going and we strive to provide the best opportunities for each individual working on our team! Here are a few reasons you will love working here:
- Competitive health, dental and vision insurance with affordable premiums
- Flexible work schedules
- Two different flexible spending account options
- Company paid life insurance with options for employee paid additional
- Performance bonus program
- Education reimbursement program
- Company paid personal leave for approved philanthropic activities
- Vacation, Sick \& Holiday leave
- Robust 401k profit sharing plan
- Opportunities for internal promotions
- Employee referral incentive program
- Rewards and gifts for service anniversaries
Disability Accommodation for Applicants \- DEPLOY's client is an Equal Employment Opportunity employer and provides reasonable accommodation for qualified individuals with disabilities and disabled veterans in its job application procedures.
Salary Context
This $85K-$199K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At DEPLOY, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($142K) sits 34% below the category median. Disclosed range: $85K to $199K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
DEPLOY AI Hiring
DEPLOY has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Huntsville, AL, US, Birmingham, AL, US. Compensation range: $199K - $199K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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