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About This Role
About Aloha Practice Management
Aloha Practice Management is a fast\-growing healthcare technology platform serving ABA organizations nationwide. We deliver end\-to\-end Practice Management, Revenue Cycle Management (RCM), and AI\-powered automation across clinical, operational, and financial workflows.
As we scale, AI is becoming foundational to our platform—from automating billing and documentation to powering intelligent workflows across the product. We are investing heavily in production\-grade AI systems that deliver real, measurable impact for providers.
The Role
We are hiring a Senior AI Engineer to own and scale production AI systems across our platform. This is a hands\-on, high\-impact role focused on building reliable, performant, and cost\-effective AI\-powered workflows used daily by healthcare providers, billers, and internal teams.
You will operate at the intersection of product, infrastructure, and data—designing and shipping AI systems end\-to\-end while helping define our AI architecture, evaluation strategy, and best practices.
What You’ll Own
· Design, build, and deploy production AI systems from concept through rollout and iteration
· Lead development of LLM\-powered workflows (e.g., claim scrubbing, intake automation, documentation, data extraction, copilots)
· Own system reliability, latency, and cost optimization for AI features in production
· Establish and evolve evaluation frameworks, monitoring, and continuous improvement loops
· Build and standardize reusable AI patterns (prompting, retrieval, agent workflows, safety guardrails)
· Partner cross\-functionally with Product, Engineering, and Clinical teams to translate real\-world problems into scalable AI solutions
· Contribute to overall AI architecture and technical direction
What Success Looks Like (First 6 Months)
· Ship multiple production AI workflows that improve efficiency in areas like billing, intake, or documentation
· Establish evaluation \+ monitoring frameworks for LLM\-based features
· Improve reliability and reduce manual operational workload through AI automation
· Define and implement best practices for building and deploying AI features at scale
Tech Environment
· Languages: Python, Node.js, .NET
· AI/LLMs: OpenAI, Azure OpenAI, Anthropic, Bedrock
· Frameworks: RAG, agent orchestration, structured outputs
· Data: SQL Server, Postgres, Redis, vector databases
· Cloud \& Infra: Azure, AWS, Docker, Kubernetes
· Observability: OpenTelemetry, Prometheus, Grafana
What You Bring
· 7–10\+ years building and shipping production software systems
· Proven experience deploying AI/LLM\-powered features into production used by real users
· Strong understanding of system design, performance, and scalability
· Experience working with LLMs, retrieval systems (RAG), or AI agents in production environments
· Familiarity with evaluation, monitoring, and iteration of AI systems
· Ability to balance speed, quality, cost, and reliability in real\-world systems
· Strong ownership mindset and ability to operate independently in ambiguous environments
Nice to Have
· Experience in healthcare, RCM, or HIPAA\-compliant environments
· Experience building internal AI tooling or developer platforms
Why This Role
· Own and shape AI systems in production, not just experiments
· Work with real\-world, high\-impact data and workflows in healthcare operations
· Build automation that directly improves efficiency for providers and billers
· Help define the AI architecture and roadmap for a rapidly scaling platform
Benefits
· 70% employer\-covered health, dental, and vision
· 401(k) with 4% match
· Cell \& internet stipend
· Profit sharing
Pay: $120,000\.00 \- $150,000\.00 per year
Application Question(s):
- Have you personally built and shipped at least one AI/LLM\-powered feature to production (customer\-facing or internal)?
- Have you helped teams ship and operate LLM/AI features and/or improved engineering productivity including reliability/cost guardrails?
- Have you used the following AI/LLM platforms in production: OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, Hugging Face?
- How many years of experience do you have with tools such as: GitHub Copilot, Claude Code, Cursor, Kiro, Kilo?
- This position is based in the Dallas–Frisco area, currently remote but with a hybrid schedule soon requiring 2\-3 days per week in\-office. Are you able to commit to this schedule?
- How many years experience do you have building production software systems?
- Do you have experience with Healthcare or HIPAA?
- Will you now or in the future require sponsorship for employment visa status?
- Do you have 7\+ years of hands\-on software engineering experience building production systems (APIs/services/workflows)?
Work Location: Remote
Salary Context
This $120K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At AlohaABA, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($135K) sits 25% below the category median. Disclosed range: $120K to $150K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
AlohaABA AI Hiring
AlohaABA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $150K - $150K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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