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About This Role
\*\*\*This role is contingent upon award\*\*\*
Corner Alliance is a dynamic, growing consulting firm that devotes itself to providing an enriching employee experience while working in meaningful ways to create results for the government. We are currently seeking an Agentic AI \& GIS Solutions Consultant with 5\+ years of experience to join our team and fully embrace our commitment to deliver, grow and thrive. You will help alert originators build, validate, and phrase alert areas with the same speed and rigor the co\-pilot brings to message text, while keeping all geometry computation deterministic and human\-approved.
About the Role:
As an Agentic AI \& GIS Solutions Consultant supporting our Federal government client, you will be trusted to design, build, and integrate the geospatial components of an agentic AI system — combining Esri ArcGIS\-based geospatial engineering with LLM orchestration, structured data workflows, and secure API design. You will work alongside emergency alerting SMEs, project leadership, and client stakeholders to ensure alert\-area geometry is authoritative, standards\-compliant, and clearly communicated.
Key Responsibilities include *(but are not limited to)*:
- Architect and build the Polygon and Geo\-Targeting Agent, importing authoritative hazard geometry — NWS warning polygons, wildfire perimeters, plume models, and jurisdictional GIS layers — and managing pre\-drawn, jurisdiction\-vetted evacuation zones
- Build on Esri ArcGIS as the geospatial foundation, consistent with the platform alerting vendors already build on
- Develop deterministic geometry validation logic detecting self\-intersections, slivers, holes, and duplicate points
- Design and maintain authoritative alert geometry storage in CAP and GeoJSON vector form
- Develop REST API endpoints for the geospatial agent, including /importGeometry, /validatePolygon, and /suggestGeoLanguage, following the same vendor\-agnostic, CAP\-centric pattern as the co\-pilot's message endpoints
- Build overshoot/undershoot analysis that compares proposed polygons to hazard footprints and surfaces the over\- vs. under\-alerting tradeoff to alert originators before send
- Ground and test the agent against real geometry and incorporate feedback from GIS practitioners and alert originators
- Coordinate with the broader Warning Author agent architecture (MDD, Pre\-Incident Template, CAP Validation, and Continuous Learning agents) to keep geospatial and message\-drafting behavior consistent
- Apply prompt engineering and agentic/RAG techniques where the LLM phrases geo\-targeted language (e.g., locally recognized streets, landmarks, zone names)
- Implement secure, compliant cloud hosting (AWS government\-compliant environment) with human\-in\-the\-loop controls, data minimization, and audit logging aligned to NIST AI RMF and NIST 800\-53
- Document system architecture, data models, geometry validation rules, and APIs; participate in stakeholder demos, technical reviews, and working groups
- *Carry out our Commitments to Deliver, Grow, and Thrive*
Location:
You will work as part of a dynamic team remotely. You will work at the client site dependent on contract requirements.
Required Experience/Skills:
- Bachelor's degree or higher in GIS, geography, geospatial engineering, computer science, data science, or a related field
- Five or more years of experience in geospatial software development, GIS analysis, or geospatial data engineering
- Hands\-on experience with Esri ArcGIS (ArcGIS Pro, ArcGIS Online/Enterprise, ArcGIS Location Platform, and/or the ArcGIS API for Python or JavaScript)
- Working knowledge of geospatial data standards and formats: GeoJSON, Shapefile, vector polygon geometry, and topology validation (self\-intersections, slivers, hole detection, polygon simplification)
- Proficiency with Python, JavaScript, and SQL; experience with at least one major cloud platform (AWS preferred, Azure, or GCP), including secure/government\-compliant environments
- Experience designing and integrating REST APIs for geospatial or structured\-data services
- Some hands\-on exposure to AI/ML or LLM\-enabled solutions (prompt engineering plus retrieval\-augmented generation or agentic workflows), sufficient to collaborate closely with the AI/LLM engineering team
- Ability to translate stakeholder and policy requirements (e.g., FCC WEA geo\-targeting rules) into technical geospatial features and validation logic
- Working knowledge of containerization (Docker), version control (Git), and CI/CD practices
- Strong analytical, documentation, and cross\-functional communication skills
- *US citizenship or permanent resident and the ability to pass public trust clearance or suitability*
Preferred Experience/Skills:
- Experience with emergency alerting, public safety communications, or IPAWS/CAP (Common Alerting Protocol) data
- Familiarity with NWS warning polygons, wildfire perimeter data, plume models, or the OpenFEMA historical alert archive
- Esri certification(s), or experience integrating ArcGIS with third\-party alerting or emergency management platforms (e.g., Everbridge, Genasys)
- Experience applying AI risk management frameworks such as the NIST AI RMF, security controls, audit logging, or human\-in\-the\-loop governance
- Familiarity with federal AI policy guidance such as OMB M\-24\-10 or successor memoranda
- Experience with FedRAMP\-authorized cloud environments
- Experience with accessibility, Section 508, or access and functional needs (AFN) considerations in public\-facing systems
*Don’t think you have everything for this role but are still very interested? Please don’t hold back from applying because you may not have it all. You can learn and grow with us. We're looking for someone who is coachable, unflappable in navigating challenges, resourceful in learning new skills, innovative in challenging the status quo, excellent in writing, analytical in thinking, skilled in prioritization, and a rapport builder with clients and colleagues.*
*About Us:*
*Corner Alliance offers a comprehensive and competitive benefits package for full\-time employees including 401k matching (4%), PTO (3 weeks to start, 4 weeks (2\-5 years) and 5 weeks (5 years\+)), health, dental, vision, short\- and long\-term disability, FSA accounts, 4 weeks of paid parental leave, 11 paid holidays (including your birthday off), fitness \& cell phone reimbursements, monthly all hands update meetings, annual in\-person all hands team building day and evening out, regular check\-ins for professional growth goals, semi\-monthly one on one performance manager meetings, a social team that coordinates monthly events, use of technology like Slack to keep us connected and collaborative, and overall, a company culture dedicated to a highly engaged team.*
*Corner Alliance is an equal opportunity employer and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex (including pregnancy, gender identity, and sexual orientation), national origin, disability status, genetics, protected veteran status, or any other basis covered by applicable law. We are dedicated to building a talented workforce that reflects the strength of our society and our shared commitment to excellence. In compliance with the Americans with Disabilities Act Amendments Act (ADAAA), if you have a disability and would like to request an accommodation in order to apply for a position with Corner Alliance please call (202\) 754\-8120 or email* *[email protected]**. Corner Alliance participates in the E\-verify program and will provide the Federal Government with Form I\-9 information to confirm work authorization in the U.S.*
*Visit us at* *www.corneralliance.com* *to learn more.*
*Securing Your Data:*
*Beware of fake employment opportunities using Corner Alliance’s name. Corner Alliance will never ask you to provide payment\-related information during any part of the employment application process (i.e., ask you for money), nor will Corner Alliance ever advance money as part of the hiring process (i.e., send you a check or money order before doing any work). Further, Corner Alliance will only communicate with you through our ATS system JazzHR and/or emails that are generated by the corneralliance.com automated system – never from free commercial services (e.g., Gmail, Yahoo, Hotmail) or via WhatsApp, Telegram, etc. If you received an email purporting to be from Corner Alliance that asks for payment\-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at* *[email protected]. If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the* *U.S. Federal Trade Commission.*
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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 Corner Alliance, 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.
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.
Corner Alliance AI Hiring
Corner Alliance has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
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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