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About This Role
AI Product Engineer
Birmingham, AL · Full\-time · In\-person (with real time
alongside the team you're building for)
*Build an AI product people actually use — and own the whole thing!*
Most engineers are shipping tickets into a codebase they'll never demo, for users they'll never meet. Here's the opposite job.
DEPLOY's client runs urgent care and primary care clinics across six states, bringing walk\-in healthcare to small\-town America.
Behind every clinic visit is a mountain of billing and insurance work — the revenue cycle — handled by real people doing repetitive, detail\- heavy work all day. We've built a working beta of an internal artificial intelligence (AI) agent tool that takes that grunt work off their plates. It works. Now it needs an owner.
That's you. One person, one product, end to end: you write the code, you decide what to build next, you sit with the people using it, and you make it better every single week.
*The big AI labs call this kind of work forward deployed engineering — embedding an* *engineer directly where the work happens and building the solution on the spot, with the* *people who'll use it.*
We're doing it pointed inward: your customer is our own revenue cycle team, and they sit down the hall, not behind a sales cycle.
*What you'll do*
Build. Take our beta and turn it into the product every revenue cycle employee reaches for daily. You'll write real code — interfaces, integrations, agent workflows— in a modern stack on Azure.
Own the product. No product manager hands you specs. You watch how the team works, find the friction, decide what matters, ship it, and measure whether it actually helped.
Work inside a real compliance perimeter. Healthcare AI has rules under HIPAA, the federal health\-privacy law: protected health information (PHI) stays on our machines, models are accessed through a compliant Azure setup, everything authenticates through Microsoft Entra. The architecture is already designed — you'll build within it and, in the process, learn the regulated\-AI engineering skills that most engineers never get to touch and that companies are desperate for.
Drive adoption. Ship it, teach it, watch people use it, fix what confuses them. Your scoreboard is simple: do people choose to use what you built?
Work directly with the CEO. Weekly product reviews with the person who can say yes on the spot. No layers.
*Who we're looking for*
A software developer with roughly 1–3 years of experience who's realized they care more about what gets built and whether it works for people than about engineering for its own sake
Strong enough to build alone — you'll be the only engineer on this product, with executive support but no senior dev reviewing your pull requests
Already tinkering with large language models (LLMs) and agents — side projects absolutely count, and we'd rather see one scrappy thing you actually shipped than a resume full of buzzwords
Able to sit with a non\-technical teammate, understand their workday, and explain your work in plain English.
Self\-directed, curious, and comfortable with figure it out as a job description.
You do not need healthcare experience. You do need to give a damn about the people using your software.
A note on the bar: we're hiring early\-career on purpose. We think the best builders can already operate at the level of engineers with a decade on them — and this seat is designed to prove it. That also means we're picky. If you're the person your team quietly routes the hard problems to, the one who shipped something last year you technically weren't supposed to know how to build yet, this job was written for you
*What's in it for you*
Ownership you can't get anywhere else at this stage of your career. This is afounding\-engineer seat for our internal AI function. As it grows, you're first in line to lead it.
A portfolio\-defining product. I built and own the AI agent platform used daily by a multi\-state healthcare company's revenue cycle team, inside a HIPAA\-compliant architecture is a sentence that will open doors for the rest of your career.
Real users, real impact, fast feedback. You'll see your work save people hours in the same week you ship it.
Startup: If we are successful in early work, this will become a true startup and you would be a founding part of.
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 DEPLOY, 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.
DEPLOY AI Hiring
DEPLOY has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Huntsville, AL, US, Birmingham, AL, US. Compensation range: $199K - $199K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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