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About This Role
Overview:
Abile Group has an exciting and challenging opportunity for a ServiceNow Developer on a long\-term contract providing Enterprise Management services supporting an Intelligence Community customer. All the personnel on the team will work together to support Multisourcing Service Integration capabilities to ensure the successful Integration and Operations of the IC, Managed Service Providers, and Legacy Service Providers.
The right candidate will possess the below skills and qualifications and be ready to handle all responsibilities independently and professionally.
Responsibilities:
- Develop and maintain machine learning pipelines and applications using Python and contemporary machine learning frameworks.
- Implement and optimize algorithms for integrating and deploying large language models (LLMs).
- Build RESTful APIs and microservices to serve machine learning models in production environments.
- Write clean, maintainable, and well\-documented code, adhering to object\-oriented programming principles.
- Collaborate with cross\-functional teams to understand requirements and convert them into technical solutions.
- Manage training data, model artifacts, and application state using SQL, NoSQL, and vector databases.
- Containerize machine learning applications with Docker to ensure consistent deployment across environments.
- Use Git for version control and participate in code reviews to maintain code quality.
- Conduct testing and debugging of machine learning applications to ensure reliability and accuracy.
- Support the deployment and monitoring of AI and machine learning models in cloud environments.
- Stay up to date with emerging trends in machine learning, LLMs, and AI engineering best practices
Qualifications:
Clearance Required: TS/SCI with CI Poly. Degree and Years of Experience: Bachelor's degree in computer science, software engineering, data science, or a related technical field, plus five years of professional experience in software development or machine learning engineering. Required Skills:* Strong proficiency in Python programming, with a thorough understanding of object\-oriented programming concepts, design patterns, data structures, and algorithms.
- Experience with development tools and practices, including Git version control, Docker containerization, and database management (SQL and/or NoSQL).
- Knowledge of large language model technologies, including familiarity with orchestration frameworks such as LangChain and LangGraph.
- Understanding of retrieval\-augmented generation (RAG) architectures and vector databases (including ChromaDB, Pinecone, Weaviate, or similar) for building intelligent retrieval systems.
- Strong problem\-solving skills, attention to detail, excellent communication abilities, and eagerness to learn within a collaborative team environment.
Desired Skills:* Master's degree in computer science or a related field.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud, and knowledge of MLOps practices for machine learning model deployment and monitoring.
- Experience with container orchestration and DevOps, including Kubernetes, Rancher, CI/CD pipelines, and infrastructure automation tools like Ansible.
- Familiarity with enterprise platforms such as ServiceNow, SAP, Tableau, or Splunk.
- Contributions to open\-source machine learning projects and familiarity with Agile development methodologies.
About Abile Group, Inc.:
Abile Group, founded in July 2004 to support the Intelligence Community and its contractors across Enterprise Analytics, IT \& Systems Engineering, and Program \& Project Management, merged with Valiant Solutions in January 2026 \- an established provider of cybersecurity technologies and services for Federal Agencies since 2005\. Together, this partnership creates a stronger, more integrated cybersecurity organization with expanded opportunities for employees, deeper technical collaboration, and a unified mission. With significant experience serving the Federal Government, we remain dedicated to our employees and clients and seek high‑performing professionals who excel at providing guidance, developing solutions, and delivering implementation support that blends industry best practices with client expertise and Abile’s broad technical capabilities.
Hiring Statement:
Abile is committed to hiring the most qualified and best fit person for the job \- always has, always will.
Anyone requiring reasonable accommodations should email [email protected] with requested details. A member of the HR team will respond to your request within 2 business days.
Please review our current job openings and apply for the positions you believe may be a fit. If you are not an immediate fit, we will also keep your resume in our database for future opportunities.
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 Abile Group, Inc., 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.
Abile Group, Inc. AI Hiring
Abile Group, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chantilly, 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
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