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About This Role
Job Status: Contingent upon customer funding and clearance crossover
Location: Reston, VA or Bolling AFB, DC (onsite)
Clearance Required: Must be a U.S. Citizen and possess a current and active TS/SCI clearance granted by the Department of Defense or an Intelligence Community agency. Must be able to pass a Counterintelligence (CI) Polygraph (current CI Polygraph a plus).
Background:
Assured Consulting Solutions provides strategic and innovative solutions for customer needs across the business, technology, and organizational spectrum. As a member of our team, you will have the chance to work with customers that are both Government and industry leaders and technology innovators.
We are looking for an experienced and highly motivated AI/ML Engineer to join our team to continue the development and training of our in\-house custom AI language model. Our model enables our customers to perform critical data labeling tasks over multi\-media sources such as video, images, audio, and documents.
Our AI/ML Engineer is the core mission specialist who develops, implements, and deploys innovative AI/ML and LLM\-enabled capabilities to solve mission\-critical problems. This role combines deep model engineering expertise with mission\-focused innovation, creating new features and approaches that leverage AI/ML technologies to maximize mission impact.
Responsibilities include, but are not limited to:
- Develop and automate fine\-tuning and model training pipelines using available tools or custom code.
- Develop innovative AI/ML and LLM\-enabled solutions to address specific mission challenges and operational needs.
- Design, implement, and optimize machine learning models for new mission\-critical use cases and features.
- Conduct research on novel modeling approaches, architectures, and techniques to maximize mission capability and competitive advantage.
- Work with mission leads and stakeholders to translate operational needs into technical AI/ML designs and implementation plans.
- Build and maintain MLOps and model deployment pipelines for experiment tracking, model versioning, and reliable production releases.
- Define and track model performance metrics aligned to mission success criteria and use evaluation findings to drive improvements.
- Integrate AI/ML model services into application workflows through APIs and production\-ready interfaces.
- Partner with Data Integration Engineers to utilize curated training datasets, test corpora, and evaluation frameworks.
- Collaborate with Senior Software Engineers to operationalize AI/ML capabilities within secure, mission\-focused application environments.
- Implement guardrails, monitoring, and fallback strategies for responsible and reliable AI/ML\-enabled operations.
- Analyze model behavior, identify performance gaps, and innovate on approaches to improve quality, reliability, and mission impact.
- Document model designs, assumptions, training methodologies, evaluation results, and operational guidance for sustainability and knowledge transfer.
- Support production troubleshooting and performance optimization for mission\-critical model\-serving workloads.
- Contribute to technical standards and best practices for responsible, secure AI/ML engineering in mission environments.
Required Qualifications:
- Bachelor's degree or higher in a related STEM field, or equivalent experience
- Hands\-on experience in machine learning engineering, applied AI, or model development with demonstrated model deployment to production.
- Strong software engineering skills in Python for model development, training, inference, and experimentation workflows.
- Experience developing and evaluating machine learning models (supervised, unsupervised, or reinforcement learning) in production or mission\-focused contexts.
- Demonstrated experience implementing and operationalizing LLM\-enabled applications or features, including prompting strategies, retrieval approaches, and integration patterns.
- Experience building and maintaining MLOps infrastructure, including experiment tracking, model versioning, reproducibility, and continuous deployment practices.
- Experience defining model performance metrics, conducting model evaluation, and using evaluation results to drive improvements.
- Experience deploying and operating model services in containerized environments (for example OpenShift or Kubernetes).
- Demonstrated case studies or examples of innovative use of AI/ML to solve domain\-specific or mission\-critical problems.
- Demonstrated ability to communicate technical complexity, model assumptions, and performance limitations clearly to both technical and non\-technical stakeholders.
- Understanding of secure development, secure AI practices, and deployment governance in controlled or classified environments.
Desired Qualifications:
- Experience supporting DIA or comparable intelligence community mission environments and problem sets.
- Experience with AWS and C2E cloud environments for AI/ML workload and model serving.
- Experience with advanced model\-serving frameworks, orchestration, or inference optimization.
- Familiarity with ontology\-driven data modeling or semantic technologies (for example RDF, OWL, or knowledge graphs) for structured reasoning.
- Experience with retrieval\-augmented generation (RAG), vector search, knowledge\-grounded LLM approaches, or semantic search.
- Experience with multi\-model or ensemble approaches for improved performance or robustness.
- Familiarity with DevSecOps practices and model release governance in secure environments.
- Experience evaluating and improving reliability, observability, and performance monitoring for mission\-critical AI systems.
Essential Job Functions:
The Americans with Disabilities Act (ADA) requires employers to identify the essential functions of a position to determine whether an individual is qualified. Essential job functions are the fundamental duties of the position that must be performed, with or without reasonable accommodation.
The essential functions of this position include:
- Ability to work onsite in a Sensitive Compartmented Information Facility (SCIF) environment, as required
- Ability to collaborate effectively with government customers, technical teams, and cross\-functional partners
- Ability to communicate clearly and effectively, both verbally and in writing
- Ability to operate standard office equipment and remain in a stationary position for extended periods
- Ability to analyze complex system requirements and develop technical recommendations
- Ability to contribute to systems engineering, integration, and evaluation efforts across the lifecycle
- Ability to support technical planning, risk identification, and system improvement initiatives
- Ability to work independently while contributing to team objectives in a fast\-paced environment
- Ability to manage multiple priorities and deliver high\-quality work under deadlines
Education Qualifications:
- Bachelor's degree or higher in a related STEM field, or equivalent experience
Years of Experience:
- 5\+ years w/ Bachelor's Degree, Master’s Degree, or PhD
Position Type: Full\-Time
Shift: Day
Export Control: For all positions requiring access to technology/software source code that is subject to export control laws, employment with the company is contingent on either verifying U.S.\-person status or obtaining any necessary license. The applicant will be required to answer certain questions for export control purposes, and that information will be reviewed by compliance personnel to ensure compliance with federal law. ACS may choose not to apply for a license for such individuals whose access to export\-controlled technology or software source code may require authorization and may decline to proceed with an applicant on that basis alone.
Assured Consulting Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability. Minorities, females, disabled and protected veterans are urged to apply. ACS' utilization of any external recruitment or job placement agency is predicated upon its full compliance with our equal opportunity/affirmative action policies. ACS does not accept resumes from unsolicited recruiting firms. We pay no fees for unsolicited services.
\#LI\-Onsite \#TSSCI \#AIMLEngineer \#MachineLearning \#MLOps \#LLM \#AI \#Cloud \#DIA
Salary Context
This $175K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Assured Consulting Solutions, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($197K) sits 9% above the category median. Disclosed range: $175K to $220K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Assured Consulting Solutions AI Hiring
Assured Consulting Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Reston, VA, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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