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About This Role
Overview:
The Business Technology team, which reports to the CIO, is building intelligent internal tools that help our clinical, operational, and financial teams work faster and make better decisions across ~850 clinics and 40+ brands. This is a business need supported by technology — not a “tech for tech’s sake” initiative.
We’re looking for an AI Solutions Engineer / Product Builder who can take a messy business problem, turn it into a clear technical plan, and ship something useful quickly. You’ll work hands-on with business leaders across ClinOps, Finance, HR, Marketing, and other teams to design and build practical tools, workflows, and AI-enabled solutions.
This is a builder role. You’ll spend most of your time creating, testing, debugging, and improving solutions, often with the help of AI tools that amplify your output. We want someone who is comfortable moving quickly, learning new systems, and figuring things out without waiting for perfect instructions.
Our current environment includes modern internal automation and solution-engineering platforms, with TypeScript as the primary build language and Python used where needed. Prior experience with our exact platforms is not required; what matters most is that you can adapt fast, build well, and solve real problems.
What You’ll Build
You’ll work on a mix of internal products, automations, integrations, and AI-enabled workflows, including:
AI Agents — role-specific assistants for procurement, operations, clinical education, and other business functions
Automations — multi-step workflows triggered by schedules, emails, forms, and webhooks
Internal Web Apps — React-based dashboards, forms, and operational tools
Data Infrastructure — PostgreSQL schemas, queries, and datasets that power internal tools
Agent-Orchestrated Workflows — solutions that combine data, tools, and memory to solve multi-step problems
Data Pipelines — ingesting and transforming data across internal and external sources
Custom Microservices — lightweight services, APIs, and tools that extend what the team can do
Mindset & Approach
- Business First: You’re energized by turning messy business processes into clear, logical systems.
- Product-Minded Builder: You think about the user, the workflow, and the outcome — not just the code.
- Fearless Problem Solver: You’re comfortable with ambiguity, you push forward without perfect instructions, and you know how to course-correct when the first version isn’t quite right.
- AI-Leveraged: You use AI as a force multiplier. You know how to direct it, review its output, and keep quality high while moving quickly.
- Hands-On Debugger: You like getting under the hood when something breaks and figuring out why a workflow, integration, or agent isn’t behaving the way it should.
Responsibilities: What Success Looks Like
In this role, you’ll be successful if you can:
- Turn a business request into a working technical solution
- Ask the right questions up front, then build with autonomy
- Move quickly without losing quality
- Debug issues across data, APIs, application logic, and AI workflows
- Build things that people actually want to use
- Learn unfamiliar systems and tools fast
- Use AI to multiply your output, not replace your judgment
What You Won’t Be Doing
- Managing a team of developers
- Owning enterprise cloud infrastructure or DevOps pipelines
- Spending all day on Power Platform or Power Automate
- Waiting for highly detailed tickets before you can start
- Working only in one narrow technology area
Why This Role Matters
This is a chance to help shape how Confluent Health builds internal AI-powered tools that improve operations, reduce manual work, and make teams more effective. The work is practical, fast-moving, and high-impact.
If you like being close to the business problem, enjoy building useful things, and are excited by the opportunity to work at the intersection of AI, automation, data, and software, this role should feel like a strong fit.
Qualifications: Required Core Skills
- TypeScript: Strong fluency. This is the primary language for much of the work.
- Integration Fundamentals: Strong experience with REST APIs, OAuth 2.0, webhooks, permissions, and debugging authentication issues.
- React: Comfortable building functional components, hooks, and data-driven interfaces.
- SQL / PostgreSQL: Strong schema design and querying skills, including joins, aggregates, and performance-aware queries.
- LLM / Agentic AI: Experience building with tool-calling, prompt design, agent loops, or similar AI-driven workflows.
Strongly Preferred
- Python: Comfortable reading, writing, and debugging Python for scripts, pipelines, or microservices.
- Microsoft 365 / Graph API: Experience integrating with SharePoint, Outlook, Teams, or Excel.
- AI-assisted development: Experience using AI tools to accelerate real work while maintaining code quality and judgment.
- Low-code / pro-code platforms: Experience with systems that combine workflow logic, code, and integrations.
- Healthcare or regulated-environment experience: Familiarity with operational, clinical, finance, or compliance-oriented workflows.
Helpful Differentiators
- Experience building internal tools, automations, or operational applications
- Experience with workflow orchestration or agent reliability
- Experience troubleshooting APIs, data issues, or production workflow failures
- Familiarity with persistent memory, knowledge graphs, or multi-step AI systems
About:
Confluent Health, LLC and its affiliates are equal opportunity employers committed to building a diverse and inclusive workforce. We do not discriminate based on race, color, religion, sex, sexual orientation, gender (including pregnancy, childbirth, or related medical conditions), gender identity or expression, national origin, marital status, age, disability, veteran status, or other applicable legally protected characteristics. If you need assistance or would like to request an accommodation due to a disability, please contact us at [email protected].
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 Confluent Health, 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.
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.
Confluent Health AI Hiring
Confluent Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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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