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About This Role
Company Overview:
About Us:
Atlanta\-based Incident IQ is the leading workflow management platform built exclusively for K\-12 districts. Trusted by over 2,000 districts, Incident IQ powers mission\-critical services for more than 12 million students and educators nationwide. By connecting technology and operational workflows, Incident IQ enables schools to streamline processes, reduce administrative burdens, and focus on what matters most: supporting students.
Purpose:
Incident IQ is committed to creating a future where every K\-12 district operates with seamless efficiency. When operations are unified on a single platform, districts gain the clarity and control needed to build a stronger foundation for student success. We're focused on delivering the tools, support, and partnerships that help make that vision a reality.
Mission:
Incident IQ is on a mission to eliminate the friction of disconnected systems and clunky workflows that slow schools down. We're reimagining the critical work that happens behind the scenes, bringing visibility, efficiency, and impact to the processes that keep classrooms running. By streamlining the complex, automating the routine, and surfacing the insights that matter most, we can create the conditions for educators to teach, students to thrive, and districts to shape the future of education.
AI DevEx Engineer Overview:
We are looking for a pragmatic and forward\-thinking AI DevEx Engineer to join our AI Tooling team. In this role, you will be instrumental in building the skills, plugins, commands, and internal tools that empower our engineering team and cross\-functional departments to work smarter and faster.
Initially, your primary focus will be expanding our custom plugins and agentic skills for Claude Code, which is heavily utilized by our engineering teams. As we grow, you will scale these efforts into multiple targeted plugins across different engineering disciplines. We need someone who is relentlessly focused on solving real business problems through Full Stack Agentic Development (FSAD). You will architect practical AI solutions, shape the "brain" of our agents, pioneer how we test AI outputs, and clearly articulate your decision\-making process to the team. We are building complex, non\-deterministic systems, which means this role requires deep resilience, patience, and a persistent focus on long\-term goals despite inevitable setbacks.
AI DevEx Engineer Responsibilities:
- Claude Code Tooling: Design and build out robust skills, tools, and agents for our internal Claude Code plugins, eventually scaling this architecture to support multiple distinct engineering workflows.
- Agent Intelligence (The "Brain"): Manage, structure, and expand our internal reference library. You will be responsible for ensuring our agents have the right context and documentation to make highly accurate, business\-aligned decisions.
- Monitoring \& Troubleshooting: Actively review agent runs, interactions, and logs generated by the engineering teams. You will identify failure points, debug complex agentic loops, and deploy fixes to improve reliability.
- Pioneer FSAD Testing: Design and implement a comprehensive testing suite from the ground up tailored for Full Stack Agentic Development. You will establish robust methodologies to effectively test, evaluate, and benchmark non\-deterministic AI results.
- Architectural Leadership: Make sound architectural decisions across the stack (primarily utilizing .NET and React) to ensure scalability, security, and performance of our AI infrastructure.
- Process Transparency: Clearly document and explain your code, architectural choices, and the step\-by\-step process used to achieve your solutions to technical and non\-technical stakeholders.
- Mentorship \& Advocacy: Actively mentor and teach engineering teams, creating comprehensive documentation and maintaining an open, feedback\-receptive mindset toward the tools you build.
- Empathetic Development: Approach development with deep empathy for existing workflows, ensuring that new solutions enhance our current processes rather than disrupting them for the sake of 'the latest' tech.
AI DevEx Engineer Requirements:
- Technical AI Proficiency: Strong command of LLM/Agentic orchestration, including MCP integrations, AI debugging/evaluation methodologies, and core CS fundamentals. A focus on data security and privacy is critical.
- Collaboration \& Communication: Exceptional ability to listen, translate complex technical jargon into actionable insights for non\-technical stakeholders, and advocate for user\-centric solutions.
- Cultural Fit \& Pragmatism: A pragmatic approach to problem\-solving that favors simple, scalable solutions over over\-engineering. We value autonomy, adaptability to rapidly shifting AI landscapes, and a positive alignment to advancing our AI strategy.
- Development: Strong general understanding of coding and architectural understanding, with highly preferred experience in .NET (backend) and React (frontend).
What Success Looks Like:
Your customers include the engineers, Product Owners, and Designers who rely on your tools. Success means broad adoption of your solutions and measurable improvements in their daily workflows. You will be a champion of the FSAD process, delivering solutions that align with company goals while navigating feedback to solve immediate issues without losing sight of our long\-term vision. You will act as an AI Ambassador at IIQ—not only creating new frameworks but actively teaching others and serving as a key resource for those growing their AI proficiency.
Bonus Points:
- Experience with Retrieval\-Augmented Generation (RAG) pipelines, vector databases, or knowledge graphing.
- Experience building internal Developer Experience (DevEx) tools.
- Familiarity with AI evaluation (Evals) frameworks for scoring agent performance.
What makes Incident IQ different:
- We facilitate whole\-person growth where employees can develop personally as well as professionally.
- We offer an energetic and collaborative environment; everyone's opinion matters!
- We produce software that empowers K\-12 schools to run efficiently, allowing for a better classroom experience for students to THRIVE!
- We provide excellent work/life balance. Two amazing offices \- a Downtown Atlanta office location and one at Halcyon in Alpharetta!
Incident IQ offers a competitive salary based on experience with a benefits package for full\-time employees that includes medical, dental, vision, life insurance, 401k match, and paid\-time off (PTO).
*Incident IQ is an Equal Opportunity Employer*
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 Incident IQ, 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.
Incident IQ AI Hiring
Incident IQ has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Alpharetta, GA, 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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