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About This Role
About Leading Edge Skills
Leading Edge Skills LLC (LES) is an established California-based organization and federal contractor delivering leadership development, cybersecurity, artificial intelligence, data analytics, technology training, and consulting to defense agencies, civilian government organizations, and commercial clients.
Our federal portfolio includes multi-year programs with the U.S. Navy's NAVSEA/NSWCDD, NAVSUP/Navy Supply Corps School, Naval Surface Warfare Center Port Hueneme Division, and the U.S. Army. These engagements encompass leadership and advanced management development, AI and emerging technology applications, mission- and capability-based test and evaluation, digital engineering, modeling and simulation, cybersecurity education, and professional workforce development.
LES has delivered 60+ leadership development cohorts under an ongoing Navy program. Our defense program footprint extends across Dahlgren, Virginia Beach, Newport, Port Hueneme, and the San Diego region, supporting scientists, engineers, military personnel, technical professionals, and senior leaders. Our broader experience includes cybersecurity workforce development for the California Department of Rehabilitation.
Our next strategic direction is technology research and development. Building on our established defense contracting experience, LES is developing capabilities in applied AI, autonomous systems, cybersecurity innovation, intelligent decision-support, and advanced simulation. We intend to pursue long-term R&D, SBIR/STTR, prototype development, and technology commercialization opportunities with DoD agencies and other federal organizations.
We seek technically accomplished innovators who can bring original ideas, demonstrated engineering expertise, and a commitment to developing practical technologies that address defense mission challenges.
Position Overview
LES is seeking an accomplished Senior Agentic AI Engineer / AI R&D Lead to help conceptualize, architect, develop, and validate innovative AI technologies for defense and government applications.
We are particularly interested in engineers, researchers, and technical innovators who have personally designed and built functional AI systems, including agentic AI platforms, multi-agent applications, intelligent decision-support tools, autonomous workflows, and advanced analytical solutions.
The successful candidate will contribute to emerging technology research, technical feasibility assessments, software architecture, prototype development, and selected federal R&D pursuits.
This is a hands-on technical position for someone capable of personally engineering and demonstrating technology, not simply advising on AI adoption or delivering instructional programs.
We value candidates with an entrepreneurial mindset who can recognize emerging technological opportunities and help transform promising ideas into defensible research concepts and commercially viable products.
Key ResponsibilitiesAI Engineering and Prototype Development
- Design, develop, and validate sophisticated agentic AI systems and LLM-powered applications.
- Engineer autonomous and human-supervised AI agents that perform complex analytical tasks and support operational decision-making.
- Build multi-agent workflows involving planning, reasoning, orchestration, information retrieval, and external tool integration.
- Develop functional software prototypes using Python, AI frameworks, APIs, and contemporary development practices.
- Implement retrieval-augmented generation (RAG), structured data pipelines, knowledge representations, and vector databases where appropriate.
- Integrate AI components with secure backend systems, databases, cloud environments, and enterprise applications.
- Develop systems incorporating explainability, audit trails, human oversight, and reliable outputs.
- Personally test, troubleshoot, optimize, and demonstrate engineered solutions.
Applied AI Research and Innovation
- Identify innovative technical approaches to complex defense and government challenges.
- Evaluate existing technologies and determine opportunities for meaningful technical advancement.
- Formulate research hypotheses, feasibility experiments, and measurable technical objectives.
- Investigate emerging capabilities involving agentic AI, multimodal systems, advanced reasoning, optimization, and AI evaluation.
- Develop methods for assessing accuracy, reliability, robustness, scalability, transparency, and uncertainty.
- Produce technical research findings, experimental evidence, feasibility reports, and documented demonstrations.
- Contribute original ideas capable of supporting future technology development and intellectual property.
Defense R&D and SBIR/STTR Initiatives
- Evaluate DoD technology requirements and determine whether innovative AI solutions are technically feasible.
- Collaborate with LES leadership, systems engineers, defense SMEs, and other technical professionals.
- Develop technical approaches responsive to individual SBIR/STTR research topics and related defense innovation opportunities.
- Prepare technical architectures, experimental plans, development roadmaps, research milestones, and prototype specifications.
- Contribute technical documentation to government R&D proposals.
- Assess development resources, technical risks, and opportunities for transition to operational use.
- Potentially serve as a technical lead or Principal Investigator on suitable projects, subject to applicable eligibility and employment requirements.
Product Development and Commercialization
- Identify opportunities to mature research concepts into functional software products.
- Recommend scalable technical architectures and practical implementation strategies.
- Evaluate government and commercial applications of proposed technologies.
- Help develop prototype-to-product roadmaps and technology commercialization strategies.
- Support long-term collaborative R&D relationships and emerging LES technology initiatives.
Required Qualifications
- Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a closely related discipline, or equivalent demonstrated expertise.
- At least seven years of professional software engineering experience, including significant hands-on AI/ML development.
- Demonstrated experience personally architecting, coding, testing, and deploying advanced AI applications or technical prototypes.
- Advanced proficiency in Python and software development best practices.
- Hands-on experience with LLMs, agentic AI frameworks, orchestration, and API integrations.
- Experience implementing functional AI workflows beyond basic prompt engineering or chatbot configuration.
- Strong understanding of AI application architecture, structured data, system integration, and evaluation methodologies.
- Experience with Git, automated testing, debugging, and technical documentation.
- Ability to develop a proof of concept independently or lead a small technical development team.
- Strong analytical thinking, independent problem-solving, and technical communication skills.
Preferred Qualifications
- Master's degree or PhD in AI, Machine Learning, Computer Science, or a related discipline.
- Hands-on experience with LangGraph, LangChain, AutoGen, Semantic Kernel, or comparable frameworks.
- Experience with PyTorch, TensorFlow, Hugging Face, vector databases, and advanced data architectures.
- Experience with intelligent decision-support, autonomous systems, simulation, optimization, or AI evaluation platforms.
- Understanding of explainable AI, human-in-the-loop decision-making, responsible AI, and secure AI architectures.
- Experience developing AI applications for government, defense, aerospace, or mission-critical environments.
- Previous involvement in SBIR/STTR, DARPA, DoD research programs, or technology commercialization.
- Published research, patents, public code repositories, commercial AI products, or demonstrable technical inventions.
- Experience as a startup founder, principal engineer, or technical R&D lead.
Initial Areas of Technical Interest
Depending on specific opportunities, the selected professional may contribute to:
- Agentic AI for defense decision management and acquisition analysis.
- AI-enabled operational decision-support systems.
- Explainable and auditable AI recommendations.
- Human-AI collaboration and intelligent analytical workflows.
- AI model evaluation and performance benchmarking.
- Mission-oriented data analysis and predictive decision intelligence.
- Intelligent simulation and scenario evaluation.
- Secure autonomous software architectures.
These are areas of strategic interest, not a guarantee that every area will be included in the initial assignment.
Initial Engagement and Expected Deliverables
Initial projects may include:
- Assessing a specific defense R&D opportunity and its technical requirements.
- Proposing an innovative solution and explaining its technical differentiation.
- Developing a system architecture and feasibility research plan.
- Identifying prototype requirements, development resources, and research risks.
- Producing a functional proof of concept when included in an authorized paid scope.
- Establishing measurable testing and validation criteria.
- Contributing technical material to research proposals and development roadmaps.
The Candidate We Want
We are interested in builders, inventors, and technically accomplished engineers with a demonstrated ability to translate complex ideas into working software.
Candidates should possess intellectual curiosity, original thinking, technical ownership, and an interest in helping develop a long-term defense technology R&D capability.
Prior government contracting experience is helpful but not essential. Proven technical engineering accomplishments are more important.
This is not a traditional AI trainer, general AI consultant, prompt-engineering-only specialist, or purely managerial position.
How to Apply
Please submit:
- Your current resume or technical professional profile.
- Descriptions of two AI systems, applications, or prototypes you personally developed, including your specific contributions.
- Relevant GitHub repositories, publications, patents, product demonstrations, or other nonconfidential technical work samples, where available.
- A brief explanation of your research interests and why you are interested in defense technology innovation.
- Attitude required:
- Honesty, humility, and integrity
- Inclusive and respectful
- Strong work ethic
- Passion for learning
- Comfortable with autonomy
- Eager to add new skills and grow professionally
- Skills that are preferred, but not required:
- Curiosity
- Assertiveness
- A love of IT and/or technology
Job Types: Part-time, Contract
Pay: $100.00 - $150.00 per hour
Work Location: Remote
Salary Context
This $208K-$312K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Leading Edge Skills, 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. This role's midpoint ($260K) sits 23% above the category median. Disclosed range: $208K to $312K.
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.
Leading Edge Skills AI Hiring
Leading Edge Skills has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $312K - $312K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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.
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