Interested in this AI/ML Engineer role at Command Post Technologies, Inc.?
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About This Role
Description:
The AI Systems Engineer is responsible for designing, building, and delivering practical artificial intelligence and machine learning solutions that support internal research, development, and business applications.
This position combines hands-on software development, deep learning, and systems architecture to translate customer needs and emerging ideas into clear technical requirements, reliable solution designs, and working products. The role will support the development and deployment of advanced AI capabilities, including agentic AI systems operating in secure, air-gapped, on-premises, and edge environments.
The ideal candidate is a curious, self-directed technical builder who takes initiative, enjoys solving complex problems, and can independently drive work from concept through implementation. This individual will own key technical efforts, mentor junior engineers, collaborate with technology leaders, and help strengthen the organization’s broader AI capabilities.
Key Responsibilities
- Design, develop, test, and deploy AI-enabled applications and solutions.
- Create and implement AI systems that leverage LLMs, autonomous workflows, tool integration, and agentic solutions.
- Develop AI-driven automation solutions to improve operational efficiency and decision-making.
- Build and integrate AI agents using commercial and open-source AI platforms and frameworks.
- Collaborate with engineers, data scientists, developers, and business stakeholders to identify AI use cases and deliver effective solutions.
- Evaluate emerging AI technologies and recommend innovative approaches to customer challenges.
- Develop technical documentation, demonstrations, prototypes, and proof-of-concept solutions.
- Support customer engagements, technical presentations, and solution demonstrations.
Requirements:
- Bachelor's degree in Systems Engineering, Computer Science, Software Engineering, Data Science, Applied Mathematics, or a related field, or equivalent practical experience demonstrated through delivered software and AI solutions.
- At least 5 years of professional development experience, including at least 3 years building, integrating, or deploying machine learning or LLM-based capabilities.
- Strong Python proficiency, including maintainable, tested code and integration of ML/LLM capabilities into larger applications.
- Hands-on experience with PyTorch or a comparable framework for training, fine-tuning, or evaluating models.
- Solid ML fundamentals, including data preparation, evaluation, and generalization, and a practical understanding of transformer models, including embeddings, tokenization, attention, and context limits.
- Experience deploying models or LLM services behind APIs, with working knowledge of latency, throughput, and GPU resource tradeoffs.
- Hands-on experience building LLM-based workflows or agents, including tool integration, RAG, context and state management, structured outputs, and retries and failure recovery.
- Ability to design repeatable evaluations of model and agent behavior, including baselines, meaningful metrics, and diagnosis of failure modes.
- Working knowledge of AI application security, including least-privilege tool access, secrets management, audit logging, and prompt-injection and data-exfiltration risks.
- Experience designing software components and interfaces, making sound technical tradeoffs, and judging when a simpler non-AI approach is sufficient.
- Experience with version control, code review, testing, and tracking tools such as Jira and Confluence.
- U.S. Citizen with the ability to obtain and maintain a Secret security clearance.
Desired Qualifications
- Active Secret security clearance.
- Experience developing AI solutions for Department of Defense or Federal Government customers, including SBIR/STTR proposals, technical capture, applied research, or transitioning prototypes into operational capabilities.
- Experience with AI platforms and agent frameworks such as Azure AI Foundry, Microsoft Copilot Studio, OpenAI, Anthropic Claude, LangGraph, LangChain, Semantic Kernel, CrewAI, or AutoGen, including multi-agent orchestration.
- Cybersecurity and secure development experience, including DevSecOps practices such as CI/CD, automated security testing, containerization, or infrastructure as code.
- Experience deploying AI systems in air-gapped, on-premises, or resource-constrained edge environments, including managing offline dependencies and model artifacts and configuring GPU workstations, servers, or edge hardware.
- Familiarity with the NIST AI Risk Management Framework or comparable AI risk and assurance practices.
- Experience with model optimization, such as fine-tuning, distillation, quantization, or multi-GPU and distributed inference.
- Experience with MLOps practices such as experiment tracking, data and model versioning, deployment, and production monitoring.
- Experience with Rust or another systems programming language.
About us: We are Command Post Technologies, Inc. (CPT). CPT is a Service-Disabled, Veteran-Owned Small Business (SDVOSB), providing engineering services in the areas of Cyber Security, Software Development, Test & Evaluation, and Strategic Planning. CPT employees appreciate working in a caring environment that promotes a healthy work-life balance. As individuals, we come together as a team, supporting a culture rooted in our core principles of integrity, determination, and innovation. In all of CPT’s collaboration efforts, our team prioritizes communication, accountability, and being resourceful in order to maximize efficiency and results.
What’s In It for You
- Great health benefits (for more info contact us directly)
- Leadership training
- 401k with generous company matching
- Employee Stock Ownership Plan (ESOP)
- Career professional development
- Tuition reimbursement
- Flexible hours
- Work/Life balance
- Rewards and recognition
- Paid Time Off
- Parental Leave
Command Post Technologies, Inc. (CPT) is a Service-Disabled Veteran-Owned Small Business (SDVOSB) founded in 2008 and headquartered in Suffolk, VA with personnel in various states including Virginia, Maryland, Florida, and Texas. With 2/3 of our staff being former military, CPT firmly believes in employing veterans. Command Post Technologies, Inc. is a unique provider of innovative solutions that enhance our corporate clients’ productivity and empower our government clients with the ability to protect against all enemies: foreign and domestic. CPT adapts its successful military experiential approach to the needs of leaders in a global business environment and provides an elite leadership curriculum that results in a world-class, leadership-altering event.
Command Post Technologies Inc. (CPT) is an Equal Employment Opportunity and Affirmative Action employer. We consider applicants without regard to race, color, religion, age, national origin, ancestry, ethnicity, gender, gender identify, gender expression, sex, sexual orientation, marital status, veteran status, disability, genetic information, citizenship status, or membership in any other group protected by federal, state, or local law. We take Affirmative Action to ensure equal opportunities for employees and potential employees without regard to race, color, religion, age, national origin, ancestry, ethnicity, gender, gender identity, gender expression, sex, sexual orientation, marital status, veteran status, disability genetic information, citizenship status, or membership in any other group protected by federal, state, or local law.
We abide by the Pay Transparency Nondiscrimination Provision and will refrain from discharging or otherwise discriminating against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants. An exception exists where the employee or applicant makes the disclosure based on information obtained while performing his or her essential job functions.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Command Post Technologies, Inc., 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Command Post Technologies, Inc. AI Hiring
Command Post Technologies, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Orlando, FL, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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