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About This Role
CORAS is a growth-stage company delivering cutting-edge, AI-powered decision intelligence to the Department of Defense and other federal organizations. Our primary clients include the U.S. Navy Program Offices, PEOs (Program Executive Offices), and Pentagon-level Portfolio and Requirements Offices. We thrive in a dynamic environment and are looking for proactive, customer-facing professionals who are energized by fast-paced problem solving, empowered to lead, and excited to make a tangible impact. Role Overview: CORAS is seeking an Agentic AI Solutions Engineer to help customers design and deploy intelligent decision workflows using the CORAS Enterprise Decision Management platform. In this role, you will work directly with mission stakeholders across the Department of Defense to translate complex operational challenges into AI-enabled decision solutions. You will configure data models, build intelligent dashboards, and implement agentic workflows that automate analysis, generate insights, and support mission-critical decision making. This is a hybrid role combining solution engineering, analytics, and AI workflow design. Successful candidates are comfortable working with modern enterprise platforms (Power Platform, ServiceNow, Salesforce, etc.), integrating data sources, and configuring automated decision agents that help organizations move from static reporting to intelligent, AI-driven operations. The ideal candidate is part solutions engineer, part systems thinker, and part customer advisor, someone who enjoys translating complex problems into practical, deployable solutions in dynamic mission environments.Key Responsibilities:* Engage directly with mission stakeholders to deeply understand analytical requirements, strategic priorities, and operational challenges.
- Leverage experience with tools like Power BI, Power Platform, ServiceNow, or Salesforce to create user-driven, scenario-based solutions.
- Assist in setting up what-if scenarios, AI agents, and automated workflows to support predictive and prescriptive decision-making.
- Present accurate, consistent, and reliable data analysis processes to optimize decision-making.
- Act as a trusted advisor to end-users, guiding them through system capabilities, troubleshooting, and ensuring adoption.
- Champion data best practices and thought leadership, keeping abreast of industry innovations in analytics, AI/ML integration, and visualization techniques.
- Troubleshoot data-related challenges with CORAS customers and prospective end users.
- Provide guidance and support to users, helping them maximize the benefits of CORAS.
- Collaborate with the CORAS development team to enhance product capabilities based on frontline user feedback and evolving customer needs.
- Drive the continuous improvement of data processes, standards, and methodologies, contributing to CORAS’s growth as a best-in-class decision intelligence platform.
Qualifications:* Customer-facing experience working on technology or analytics implementations — consulting, pre-sales engineering, customer success, or product support.
- Skilled with modern enterprise platforms such as ServiceNow, Salesforce, Quickbase, Appian, Smartsheet, or Power Platform (esp. Power Apps/Automate) and/or BI tools like Qlik, Tableau, or Power BI—especially when applied to building use-case-driven workflows, dashboards, or customer-facing solutions.
- Basic familiarity with data pipelines and scripting languages (such as SQL, Python, PowerShell, or JavaScript) is a plus – especially for configuring automations, transforming data, or supporting integrations.
- Comfortable with iterative, agile delivery, and working in dynamic customer environments (especially federal/DoD a plus).
- Familiar with data integration, transformation, and quality assurance across spreadsheets, cloud systems, APIs, and structured/unstructured data.
- Experience with AI/ML-enriched platforms, enterprise digital transformation initiatives, or large language models (LLMs), including prompt engineering, is a strong plus.
- Effective communication skills to convey complex insights to diverse stakeholders.
- Ability to work independently and collaboratively in a dynamic environment.
Benefits:* Competitive compensation package and performance-based incentives.
- Comprehensive health, dental, and vision benefits.
- Flexible work arrangements, including hybrid work options.
- Opportunities for continuous professional growth and development.
- Collaborative and inclusive work environment.
- Exposure to cutting-edge technologies and innovative projects.
Salary Context
This $80K-$90K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At coras, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($85K) sits 60% below the category median. Disclosed range: $80K to $90K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
coras AI Hiring
coras has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in McLean, VA, US. Compensation range: $90K - $90K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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