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About This Role
AI Developer
Location: Remote (occasional travel may be required)
Clearance:Public Trust or able to obtain
Salary: $110,000 \- $160,000
BluePath Labs is a professional services and technical solutions company designed to improve the identification, development, and application of revolutionary technologies for U.S. National Interests. BluePath's multidisciplinary experience supporting government, academia, and industry is unique among small businesses and a core differentiator of our solutions. BluePath is now an Inc. 5000 company transforming critical missions for the Departments of Defense, Homeland Security, Energy, and Commerce in a variety of domains, ranging from strategic competition and cybersecurity research and development to border, supply chain, and energy program support.
We are actively seeking an AI Developer to support Federal Government clients.
*Note:This position iscontingent upon contract award**. We are currently seeking qualified candidates to include in our proposal for an upcoming government contract. Applicants selected may be contacted for further steps if the contract is awarded.*
Work Description:
The AI Developer will design, build, and support artificial intelligence and machine learning solutions that address complex business and mission challenges. The role involves developing production\-ready AI applications, integrating models into cloud\-based environments, collaborating with technical and business stakeholders, and continuously improving system performance, reliability, and scalability. The successful candidate will work across the full AI lifecycle, from requirements analysis and solution design through deployment, monitoring, and maintenance.
Responsibilities:TheAI Developer will support tasks such as:
- + Design, develop, test, and deploy AI and machine learning solutions.
+ Build and integrate AI\-powered applications, APIs, and services into existing systems.
+ Develop, train, evaluate, and optimize machine learning models.
+ Collaborate with software engineers, data scientists, and business stakeholders to translate requirements into technical solutions.
+ Prepare, analyze, and transform structured and unstructured data for model development.
+ Implement MLOps and DevOps practices to support deployment, monitoring, and continuous improvement.
+ Develop and maintain technical documentation, architecture diagrams, and operational procedures.
+ Support cloud\-native AI solutions hosted in platforms such as Azure, AWS, or other approved environments.
+ Troubleshoot application, infrastructure, and model performance issues and implement corrective actions.
+ Stay current with emerging AI technologies, frameworks, and industry best practices.
Requirements:
- + U.S. Citizen.
+ 5\-10 years of professional experience designing, developing, and supporting software applications or data\-driven solutions.
+ Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related technical field.
+ Strong proficiency in one or more programming languages such as Python, JavaScript, Java, or C\#.
+ Experience developing, testing, and deploying cloud\-based applications in environments such as Microsoft Azure, AWS, or Google Cloud.
+ Familiarity with machine learning concepts, model development workflows, and modern AI frameworks.
+ Experience working with source control systems and collaborative software development practices.
+ Knowledge of software delivery practices, including automated testing, release management, and deployment automation.
+ Ability to troubleshoot complex technical issues and optimize application performance in production environments.
+ Strong analytical, communication, and problem\-solving skills.
+ Ability to work effectively within cross\-functional Agile teams.
Preferred Qualifications:
- + Experience with combating fraud
+ Experience with ServiceNow
Benefits:
BluePath Labs offers a comprehensive benefits package. Benefits include, but are not limitedto:employer\-sponsored healthcare plan, lifestylewellness reimbursement, Flexible Spending Account (FSA), tuitionassistance, 401(k) with company match, and paid time off for vacation / sick leave, in addition to 12 holidays per calendar year.
About BluePath
BluePath Labs combines mission and business insights with advanced technologies to deliver measurable performance improvements for our clients. BluePath is dedicated to surpassing client expectations by always living by our core values of integrity, professionalism, and resilience. BluePath's extensive experience in Government, Military, Commercial, and Academic environments is unique among small businesses and a core differentiator of our solutions. Our multidisciplinary background allows us to solve diverse and complex problems. Most importantly, we work closely with our clients to frame problems correctly,optimizeprocesses,leveragetechnologies, and implement enduring solutions. Labs are where ideas are born, experiments occur, and breakthroughs happen. It is the hallmark of BluePath's culture.
https://www.bluepathlabs.com/
BluePath Labs is an equal opportunity employer.
Salary Context
This $110K-$160K 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 BluePath Labs, 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 ($135K) sits 37% below the category median. Disclosed range: $110K to $160K.
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.
BluePath Labs AI Hiring
BluePath Labs has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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