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About This Role
Engineering \& Artificial Intelligence Talent – General Interest
Job ID\#: 26\-5019
Clearance: Any
Who We Are:
Since our inception back in 2006, Navitas has grown to be an industry leader in the digital transformation space, and we’ve served as trusted advisors supporting our client base within the commercial, federal, and state and local markets.
What We Do:
At our very core, we’re a group of problem solvers providing our award\-winning technology solutions to drive digital acceleration for our customers! With proven solutions, award\-winning technologies, and a team of expert problem solvers, Navitas has consistently empowered customers to use technology as a competitive advantage and deliver cutting\-edge transformative solutions.
What you'll do:
Navitas is seeking talented and motivated Engineering and Artificial Intelligence professionals to join our growing technology team. We are interested in connecting with experienced engineers, technologists, and emerging technology professionals who are passionate about developing innovative solutions, solving complex technical challenges, and applying modern technologies to real\-world problems.
Successful candidates may contribute to projects involving software engineering, artificial intelligence, machine learning, data engineering, cloud computing, cybersecurity, DevSecOps, systems engineering, automation, and enterprise technology modernization.
Navitas offers opportunities for professionals who enjoy working in collaborative, technically challenging environments and want to contribute to the development of secure, scalable, and innovative technology solutions supporting mission\-critical initiatives.
*Areas of Opportunity:** Artificial Intelligence \& Machine Learning
+ Generative AI and Large Language Models (LLMs)
+ Machine Learning
+ Natural Language Processing (NLP)
+ Computer Vision
+ Predictive Analytics
+ AI/ML model development and deployment
+ Prompt engineering and AI application development
+ Retrieval\-Augmented Generation (RAG)
+ AI agents and intelligent automation
+ Responsible AI and AI governance
- Software Engineering
+ Full\-Stack Development
+ Backend and Frontend Development
+ Java, Python, C\#, C\+\+, JavaScript, TypeScript, or similar languages
+ RESTful APIs and microservices
+ Enterprise application development
+ Application modernization
- Data Engineering \& Analytics
+ Data Engineering
+ Data Architecture
+ ETL/ELT
+ Data Pipelines
+ Data Warehousing
+ Big Data
+ SQL and NoSQL databases
+ Data Analytics and Visualization
- Cloud \& DevSecOps
+ Microsoft Azure
+ Amazon Web Services (AWS)
+ Google Cloud Platform (GCP)
+ Kubernetes and Docker
+ CI/CD
+ Infrastructure as Code
+ Terraform
+ GitHub/GitLab/Jenkins
+ Cloud architecture and modernization
- Systems \& Solutions Engineering
+ Systems Engineering
+ Solutions Architecture
+ Enterprise Architecture
+ Integration Architecture
+ API Architecture
+ Distributed Systems
+ Systems integration
+ Technical requirements and solution design
- Cybersecurity \& Emerging Technology
+ Cloud Security
+ Application Security
+ DevSecOps
+ Identity and Access Management
+ Security Engineering
+ Zero Trust
+ Automation
+ Emerging technology evaluation and implementation
Depending on the position and project assignment, responsibilities may include:* Design, develop, integrate, test, deploy, and maintain enterprise technology solutions.
- Apply engineering principles and modern development practices to solve complex technical challenges.
- Design and implement AI/ML solutions that improve automation, decision\-making, data analysis, and operational efficiency.
- Develop scalable software applications, APIs, services, and data pipelines.
- Analyze business and technical requirements and translate them into effective technology solutions.
- Design and implement cloud\-based, distributed, and highly available systems.
- Develop and maintain CI/CD pipelines and automated testing processes.
- Integrate AI, data, applications, and enterprise systems across complex technology environments.
- Evaluate emerging technologies and recommend solutions that improve performance, scalability, security, and maintainability.
- Develop technical documentation, architecture diagrams, system designs, and engineering specifications.
- Troubleshoot complex technical issues and perform root\-cause analysis.
- Collaborate with engineers, architects, data scientists, cybersecurity professionals, project managers, and business stakeholders.
- Participate in Agile/Scrum development activities and contribute to continuous improvement.
- Ensure solutions follow applicable security, quality, compliance, and engineering standards.
- Mentor team members and contribute to engineering best practices and knowledge sharing.
What You’ll Need:* Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, Artificial Intelligence, Mathematics, or a related technical field, or equivalent professional experience.
- Professional experience in software engineering, systems engineering, AI/ML, data engineering, cloud engineering, cybersecurity, or a related technical discipline.
- Strong analytical, problem\-solving, and technical communication skills.
- Experience working with modern software development, engineering, cloud, data, or AI technologies.
- Ability to work independently and collaboratively in a fast\-paced, team\-oriented environment.
- Demonstrated ability to learn and apply new technologies to complex technical problems.
Security Clearance Requirements:
Because Navitas supports opportunities that may involve federal government systems, sensitive information, and mission\-critical environments, security clearance requirements vary by position and assignment.
Candidates with one or more of the following are encouraged to apply:* Public Trust
- Suitability or federal background investigation
- Secret
- Interim Secret
- Top Secret
- Interim Top Secret
- Top Secret/SCI (TS/SCI)
- SCI eligibility
- Other federal security clearances or adjudicated investigations
Some positions may require candidates to obtain and maintain a specific security clearance as a condition of employment. Clearance requirements, eligibility standards, and investigation levels will vary based on the specific position.
Candidates should clearly identify their current clearance level, clearance status, and eligibility for additional clearance requirements on their resume.
Set Yourself Apart with:* Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field.
- Experience developing or deploying Generative AI, LLM, RAG, machine learning, or AI agent solutions.
- Experience with Python and AI/ML frameworks such as PyTorch, TensorFlow, Scikit\-learn, or similar technologies.
- Experience with cloud AI/ML services from Azure, AWS, or GCP.
- Experience with APIs, microservices, containers, Kubernetes, and cloud\-native architectures.
- Experience with data platforms, data pipelines, and large\-scale data processing.
- Experience with DevSecOps, CI/CD, Infrastructure as Code, and automated testing.
- Experience working in Agile/Scrum environments.
- Relevant professional certifications in cloud, AI/ML, cybersecurity, software engineering, or systems architecture.
- Active federal security clearance or current federal clearance eligibility.
What We Look For:
Navitas is interested in professionals who are:* Technically curious and excited about emerging technologies.
- Problem solvers who can tackle complex engineering challenges.
- Innovative and able to identify practical applications for AI and automation.
- Collaborative and comfortable working across technical disciplines.
- Adaptable and willing to continuously learn new technologies.
- Security\-minded with an understanding of developing reliable, secure, and responsible technology.
- Outcome\-focused, with the ability to turn ideas and requirements into working solutions.
General Interest / Talent Community
This opportunity may be used to identify candidates for current and future engineering, AI, data, cloud, cybersecurity, and emerging technology opportunities with Navitas. Specific responsibilities, qualifications, technologies, security clearance requirements, and experience requirements may vary based on the position and project.
We encourage experienced professionals and emerging technology specialists with strong engineering, AI, or technical backgrounds—and candidates who currently hold or are eligible for federal security clearances—to submit their information for consideration.
Salary: $50,000 \- $200,000*The pay range provided is a good\-faith estimate and represents the range the company reasonably expects to pay for this position at the time of posting. Individual compensation may vary based on skills, experience, education, certifications, geographic location, and business needs.*
*Equal Employer/Veterans/Disabled*
*Navitas Business Consulting is an affirmative action and equal opportunity employer. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact Navitas Human Resources.*
*Navitas is an equal opportunity employer. We provide employment and opportunities for advancement, compensation, training, and growth according to individual merit, without regard to race, color, religion, sex (including pregnancy), national origin, sexual orientation, gender identity or expression, marital status, age, genetic information, disability, veteran\-status veteran or military status, or any other characteristic protected under applicable Federal, state, or local law. Our goal is for each staff member to have the opportunity to grow to the limits of their abilities and to achieve personal and organizational objectives. We will support positive programs for equal treatment of all staff and full utilization of all qualified employees at all levels within Navitas.*
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Salary Context
This $50K-$200K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Navitas Business Consulting, 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. This role's midpoint ($125K) sits 42% below the category median. Disclosed range: $50K to $200K.
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.
Navitas Business Consulting AI Hiring
Navitas Business Consulting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Herndon, VA, US. Compensation range: $200K - $200K.
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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