Interested in this AI/ML Engineer role at Edgesource Corporation?
Apply Now →Skills & Technologies
About This Role
Company Overview
For over 25 years, Edgesource Corporation has served as an innovative technology service provider for the Department of Defense (DOD), Department of Homeland Security (DHS), Department of State (DOS), the U.S. Intelligence Community, Law Enforcement, and other federal, state, and commercial clients locally, nationally, and abroad. From providing boutique technical solutions in support of the DOD Counter Unmanned Aerial Systems (CUAS) mission set to addressing the most critical Cybersecurity threats facing our nation as a prime contractor with the DHS Cybersecurity \& Infrastructure Security Agency (CISA), a career at Edgesource is an opportunity to do meaningful, interesting, and impactful work.
Position Overview
We are seeking a highly motivated Mid\-to\-Senior Applications \& Systems Engineer to design, develop, integrate, and sustain modern software applications, cloud services, and mission\-critical systems. The ideal candidate is a hands\-on engineer with experience building full\-stack applications, automating operational workflows, integrating complex systems, and leveraging modern AI\-powered development tools and agentic workflows to accelerate delivery.
This position requires an engineer who can operate across traditional boundaries between software engineering, systems engineering, cloud infrastructure, DevOps, and AI\-enabled automation. The successful candidate will help architect and implement scalable solutions while identifying opportunities to apply AI agents, automation frameworks, and intelligent workflows to improve operational efficiency and software delivery.
Responsibilities
Software \& Application Engineering
- Design, develop, test, and maintain web applications, APIs, microservices, and backend systems.
- Build scalable and maintainable software solutions using modern programming languages and frameworks.
- Develop and integrate RESTful APIs, event\-driven architectures, and asynchronous processing systems.
- Participate in application modernization, refactoring, and technical debt reduction initiatives.
- Troubleshoot and resolve production issues across application and infrastructure layers.
Systems Engineering \& Integration
- Analyze user requirements and translate operational needs into technical solutions.
- Design and integrate complex systems spanning cloud, on\-premises, and hybrid environments.
- Support system architecture development, interface definition, and technical documentation.
- Perform system validation, testing, and performance optimization.
- Collaborate with stakeholders, product owners, and engineering teams throughout the system lifecycle.
Agentic AI \& Automation
- Design and implement AI\-assisted workflows that improve engineering productivity and operational effectiveness.
- Develop agentic solutions using modern LLM platforms, orchestration frameworks, and automation tools.
- Build AI\-powered assistants for software development, data processing, system operations, and business workflows.
- Evaluate and integrate AI coding assistants, autonomous agents, and workflow automation technologies.
- Identify opportunities to automate repetitive engineering and operational tasks using AI\-enabled solutions.
DevOps \& Cloud Engineering
- Develop and maintain CI/CD pipelines and automated deployment processes.
- Support cloud\-native solutions across AWS, Azure, and hybrid environments.
- Implement containerized solutions using Docker and related technologies.
- Automate infrastructure provisioning, configuration management, and operational monitoring.
- Support application security, reliability, scalability, and observability initiatives.
Data \& Integration Engineering
- Build and maintain data ingestion, transformation, and processing pipelines.
- Support integration of structured and unstructured data sources.
- Develop automation for data movement, validation, and analytics workflows.
- Collaborate with data engineers, analysts, and AI/ML teams to support mission and business objectives.
Required Qualifications
- Active Top Secret clearance with SCI eligibility
- Bachelor’s degree in Computer Science, Software Engineering, Systems Engineering, Information Systems, or related field (or equivalent experience).
- 5\+ years of experience in software engineering, systems engineering, or application development.
- Experience with one or more programming languages such as Python, JavaScript/TypeScript, Go, Java, PHP, or C\#.
- Experience designing and consuming REST APIs and service\-based architectures.
- Experience with cloud platforms such as AWS or Microsoft Azure.
- Experience with CI/CD pipelines, source control, and modern software development practices.
- Strong troubleshooting and problem\-solving skills.
- Ability to work independently and collaboratively in Agile environments.
Desired Qualifications
- Experience building or integrating AI\-powered applications and agentic workflows.
- Experience with LLM platforms such as OpenAI, Anthropic Claude, Azure OpenAI, or similar technologies.
- Familiarity with AI orchestration frameworks, RAG architectures, vector databases, and workflow automation platforms.
- Experience with Databricks, data lakes, or large\-scale analytics environments.
- Experience with containerization and orchestration technologies.
- Experience supporting government, defense, intelligence community, or regulated environments.
- Familiarity with cybersecurity, secure software development, or RMF principles.
- Experience supporting real\-time, mission\-critical, or operational systems.
Working at Edgesource
As an ISO 9001:2015 certified and CMMI Level 3 appraised small business, Edgesource specializes in providing a variety of technical solutions to include software development, database services, enterprise networking, data center virtualization, and management support. We are always seeking top\-talent to join our team in helping to address the most critical technical challenges facing our nation.
At Edgesource, we understand that our employees are our greatest asset, and as such we offer a wide array of benefits to support the well\-being of our staff to include:
- Flexible PTO Policy \+ 11 Paid Holidays
- Flexible Work Schedules (Remote / Hybrid)
- Medical / Dental / Vision / Flexible Spending Account (FSA)
- 401k Plan with Match
- Tuition \& Professional Development Support
- Commuter Benefits
- Bonus \& Employee Referral Programs
- Career Growth Opportunities
Disclaimer
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, disability, age, sexual orientation, gender identity, national origin, veteran status, or genetic information. Edgesource is committed to providing access, equal opportunity, and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. To request reasonable accommodation, please contact our Recruiting Department by email at recruiting@edgesource.com or by phone at (703\) 837\-0550\.
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 Edgesource Corporation, 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.
Edgesource Corporation AI Hiring
Edgesource Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Alexandria, VA, 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.