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About This Role
DCCA is a veteran\-owned high\-technology company specializing in providing information technology services to a variety of government agencies and commercial enterprises since 1982\. DCCA is proud to offer a strong, competitive compensation and benefits package. Visit our website at: www.dcca.com .
AI/ML Engineer
CANDIDATES MUST HAVE AN ACTIVE TS/SCI w/ Poly
For over 40 years, DCCA has provided a broad range of IT services to government agencies and commercial enterprises, helping them to feel confident in their IT infrastructure. With DCCA, these organizations can be confident in the flexibility and skill of their IT partners, allowing them to upgrade their technology quickly and efficiently. Better yet, thanks to DCCA’s successful track record, clients can rest assured knowing DCCA can tackle any problem with ease, allowing them to focus on the work that matters.
Internally, DCCA prides itself on a culture built on integrity and inclusivity, allowing its employees to build lasting skills and relationships. As a veteran owned business, DCCA knows the importance of recruiting employees with a wide range of backgrounds, allowing for every problem to be approached by a diverse array of perspectives. Join us and be part of a team that has a people first mentality and a dedication to excellence.
Key Tasks:
- Model Deployment \& MLOps: Architect, implement, and manage scalable MLOps pipelines for the continuous integration, continuous delivery (CI/CD), and continuous training (CT) of machine learning models within AWS GovCloud environments.
- AI/ML Integration: Seamlessly integrate predictive models, Natural Language Processing (NLP) algorithms, and deep learning neural networks into production microservices and enterprise applications.
- Algorithm Optimization: Optimize machine learning models for performance, latency, and scalability, ensuring they can process large volumes of real\-time data efficiently.
- Cybersecurity \& Defensive AI: Leverage machine learning for cybersecurity by integrating AI\-driven threat detection, fraud prevention algorithms, and defensive cyber operations into system architectures.
- Edge AI \& System Sustainment: Design lightweight AI models for Edge AI applications, ensuring high\-performance computing capabilities are pushed closer to data sources where required by the mission.
- Infrastructure Management: Utilize cloud\-native AI/ML services (e.g., AWS SageMaker) and container orchestration platforms (e.g., Docker, Kubernetes) to provision and scale AI infrastructure dynamically.
- Compliance \& Security: Ensure all AI/ML implementations strictly comply with the Risk Management Framework (RMF), NIST 800\-53 security controls, and federal guidelines for algorithmic fairness and data privacy.
Required Skills:
- Experience: Minimum of 5\+ years of hands\-on experience in software engineering with a primary focus on deploying and operationalizing AI/ML models.
- Programming Languages: Expert\-level proficiency in Python, as well as strong capabilities in Java, C\+\+, or Go.
- Machine Learning Frameworks: Deep technical knowledge of industry\-standard AI/ML frameworks such as TensorFlow, PyTorch, Keras, and Scikit\-Learn.
- Cloud \& MLOps: Proven experience with AWS machine learning services (e.g., SageMaker) and building automated MLOps pipelines using Git, Jenkins, or GitLab CI.
- Clearance: TS/SCI w/Poly
Desired Skills :
- Prior experience in engineering AI solutions for federal agencies, including the Department of Defense (DoD), Defense Information Systems Agency (DISA), or the Intelligence Community.
- Familiarity with Cognitive Automation and Robotic Process Automation (RPA) tools.
- Experience with advanced AI disciplines, including Deep Learning for Signal Processing and Electronic Warfare integration.
- Active industry\-recognized certifications (e.g., AWS Certified Machine Learning – Specialty, SAFe Agile Certification).
Education/Certifications:
- Education: Bachelor’s or master’s degree in computer science, Artificial Intelligence, Machine Learning, Data Engineering, or a related highly technical discipline.
The proposed salary range for this position in Virginia is 195,000 to 259,000\. Final salary will be determined based on various factors. Our comprehensive benefit offerings include healthcare, retirement plan, paid disability and life insurance programs, employee assistance program, paid and unpaid leave programs, education assistance, and wellness initiatives.
At DCCA, we believe the key to providing our clients with unrivaled services starts with retaining top talent, something we’re able to do through our consistent commitment to building culture and comprehensive benefits.
Competitive Compensation: While salary at DCCA is determined by various factors, we are committed to making sure our salaries reflect the skill and expertise of our employees. In addition, each year we perform an annual salary review ensuring pay is equitable across both the company and industry at large.
Growth Opportunities: DCCA makes it a priority to help you grow and support your career advancement. From upskilling programs to recertification support, to professional development opportunities, we’re here to help you grow your career and create lasting relationships.
Emphasis on Inclusivity: DCCA’s culture emphasizes respect, equity, and opportunity and is supported by an array of business resource groups and other opportunities for connection.
Empowering Health: DCCA’s benefits which encompass healthcare, paid time off, and flexible 401(k) options encourage you to live a healthy and fulfilling life, both in and outside of work. Learn more about our total benefits package on our Benefits page.
Mission Focused Work: From the defense industry to health IT management, DCCA allows you to work on innovative projects whose outcomes improve people's lives and solve today’s IT problems.
Equal Opportunity Employer including Disability/Vets
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 DCCA, 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.
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.
DCCA AI Hiring
DCCA has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Chantilly, VA, US. Compensation range: $260K - $260K.
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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