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About This Role
As a Lead Artificial Intelligence Engineer for Camgian Labs, you will lead a highly technical team developing next-generation AI/ML capabilities for national security missions.
You will remain an active technical contributor while owning architecture, technical execution, and delivery across customer-funded and internal R&D efforts. You will work directly with customers, stakeholders, and multidisciplinary teams to turn mission needs into practical, effective solutions from early-stage concepts through prototype delivery.
This role is ideal for an experienced engineer who wants to remain hands-on while leading engineers, shaping technical direction, and delivering complex technical work.
Qualifications
- Bachelor’s degree in aerospace engineering, electrical engineering, computer science, computer engineering, statistics, applied mathematics, or a related field.
- 7+ years of professional experience in AI/ML development, validation, and deployment.
- Demonstrated proficiency in algorithm development and implementation using Python or similar programming languages.
- Strong mathematical background and knowledge of AI, ML, deep learning, and modern data science techniques.
- Experience leading technical efforts across multiple engineers or workstreams.
- Excellent written and verbal communication skills.
- Experience interacting directly with customers and stakeholders.
- Experience working in Agile or iterative development environments.
- Ability to travel up to 10% within the United States.
- Eligibility to obtain or maintain a U.S. Government Security Clearance which requires U.S. citizenship.
Desired Skills
- Master’s or PhD in a relevant technical field.
- Experience leading engineering teams or serving as a technical lead.
- Experience conducting design reviews, code reviews, and architecture discussions.
- Experience mentoring engineers and supporting professional development.
- Experience deploying AI models into operational or cloud environments.
- Experience with TensorFlow, PyTorch, scikit-learn, OpenCV, or similar frameworks.
- Strong systems engineering mindset and ability to manage technical tradeoffs.
- Experience applying AI/ML to air, space, or missile defense missions.
Key Responsibilities
- Lead the design, development, testing, and deployment of AI/ML software prototypes.
- Own technical direction and architecture for assigned efforts and ensure consistency across projects.
- Drive technical planning, estimation, and execution across customer-funded and internal R&D efforts, including defining and tracking technical milestones and deliverables.
- Conduct design and code reviews to ensure quality, scalability, and maintainability.
- Identify and manage technical risks related to data, models, system integration, and dependencies.
- Serve as the primary technical interface with customers, presenting designs, results, and tradeoffs clearly.
- Lead and mentor a team of engineers, supporting team growth, capability development, and scaling of engineering practices.
- Improve team effectiveness through structured planning, clear prioritization, and timely resolution of technical and organizational blockers.
- Ensure engineering efforts align with customer needs and company objectives.
- Lead or contribute to proposal development, including shaping technical approaches, estimates, and solution strategies.
Success in This Role
- Deliver technically sound solutions on schedule across multiple efforts.
- Reduce technical risk early through strong architecture and planning.
- Improve team throughput and quality through effective leadership and coaching.
- Build credibility with customers through clear communication and consistent execution.
- Communicate technical progress, risks, and tradeoffs clearly to stakeholders.
- Maintain meaningful hands-on technical contributions while leading the team.
Why Join Us
- Join Camgian Labs, the company’s advanced R&D organization focused on developing next-generation capabilities for air, space, and missile defense.
- Work on mission-critical AI/ML challenges supporting national security.
- Collaborate with experienced engineers, scientists, and operators on high-impact problems.
- Influence technical direction in a growing advanced R&D organization.
- Ability to move from concept to prototype quickly.
- Lead technical efforts while remaining deeply hands-on.
About Camgian
Camgian’s mission is to deliver advanced artificial intelligence and machine learning solutions that accelerate decision-making in high-stakes environments. We develop technologies that help operators respond more effectively to threats across air and missile defense, space defense, and counter-UAS missions by improving situational awareness, speed, and decision quality.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Camgian, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Camgian AI Hiring
Camgian has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Collins, CO, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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