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About This Role
Description of the Role
As an AI Engineer at Vytalize Health, you will design, build, and maintain agentic systems and LLM\-powered applications that automate complex healthcare workflows and accelerate our ability to deliver data\-driven clinical solutions. Working at the intersection of applied AI and healthcare, you will build agents that orchestrate data retrieval, model inference, clinical logic, and tool use to solve problems that traditionally require manual effort or specialized expertise.
You will work cross\-functionally with data engineering, platform, product, and clinical teams to identify high\-impact opportunities for AI automation—from data source onboarding to clinical decision support to evidence synthesis. Your focus will be on building production\-grade agentic systems with rigorous validation, clear confidence scoring, and human\-in\-the\-loop oversight to ensure reliability in a regulated healthcare environment. You will establish patterns, best practices, and tooling that allow the organization to scale AI\-driven automation across multiple domains. You will measure agent performance and impact—tracking accuracy, hallucination rates, and real\-world clinical outcomes. You will be part of a growing AI team that values both cutting\-edge AI capabilities and deep healthcare domain understanding.
Primary Responsibilities
- Design, build, and maintain agentic systems and LLM\-powered applications that automate healthcare workflows, data pipelines, and clinical decision support — from conception through production deployment
- Build and orchestrate agents using LLM APIs (OpenAI, Anthropic, etc.) and agentic frameworks (LangChain, LangGraph, CrewAI, or custom orchestration) to solve complex, multi\-step healthcare problems
- Develop prompt libraries, agent instructions, and reusable "skills" that improve agent accuracy, consistency, and reliability across different use cases and data domains
- Build validation and confidence\-scoring layers that flag low\-confidence agent decisions for human review before production deployment; establish guardrails and review workflows for agent\-authored code and outputs
- Own end\-to\-end delivery of AI\-automated systems — from problem scoping and requirements gathering through agent development, testing, and validated production deployment
- Implement rigorous evaluation and QA frameworks for agentic systems — including golden datasets, test cases, output validation, hallucination detection, and regression testing
- Establish and maintain evaluation metrics for agent performance, reliability, and clinical appropriateness; measure agent accuracy, hallucination rates, clinical validity, and real\-world impact
- Implement observability, evaluation, and regression testing frameworks specific to agentic systems — decision tracing, lineage logging, and performance tracking
- Collaborate with data engineering and platform teams to integrate agent\-built outputs (dbt models, transformation logic, recommendations) into existing data architectures and clinical workflows
- Ensure all agentic systems comply with healthcare regulations (HIPAA, FDA guidance on AI/ML) and responsible AI practices — including explainability, auditability, and clinician trust
- Continuously evaluate new LLM models, agent frameworks, prompt engineering techniques, and tooling; recommend adoption or migration based on healthcare\-specific requirements (accuracy, cost, latency, regulatory alignment)
- Partner with data engineering to establish robust data validation and input validation layers for agents — agents are only as good as the data they operate on
- Lead experimentation and measurement of AI\-automated systems impact on speed, quality, compliance, and cost across healthcare workflows
- Document agent architectures, prompt strategies, evaluation frameworks, and best practices for both technical and non\-technical stakeholders
- Mentor AI Connector Engineers and other team members on agentic development patterns, LLM\-powered application design, and responsible AI practices
- Work on\-call as needed to support production agentic systems, troubleshoot agent issues, and respond to performance degradation or hallucination detection
Required Qualifications
- 3\+ years of professional experience in data engineering, backend engineering, machine learning, or a related field
- 1\+ years of hands\-on experience building with LLM APIs and agentic orchestration frameworks — not just using AI coding assistants, but architecting agentic systems
- Strong Python and SQL proficiency
- Experience with cloud data platforms (AWS, Databricks)
- Solid understanding of data modeling, ETL/ELT patterns, and medallion architecture (Bronze/Silver/Gold)
- Experience building and consuming APIs
- Demonstrated experience with prompt engineering, agent evaluation, and validating LLM outputs
- Experience designing evaluation frameworks, test cases, and quality assurance for AI/ML systems
- Demonstrated ability to measure and track AI system performance through metrics and KPIs (accuracy, precision, recall, hallucination rates)
- Strong debugging and analytical skills, especially in ambiguous or novel technical territory
- Excellent written and verbal communication skills — this role requires documenting agent reasoning, decisions, and limitations clearly for both technical and non\-technical audiences
- Comfortable working in a fast\-moving environment with incomplete information and rapidly evolving AI/ML capabilities
Strong Pluses
- Experience with dbt or similar data transformation frameworks
- Familiarity with orchestration tools (Airflow, Databricks Workflows) and workflow automation
- Experience with agent evaluation and observability tooling (LangSmith, Langfuse, or custom frameworks)
- Background in healthcare, fintech, or another regulated/high\-stakes domain where AI reliability is critical
- Experience building internal developer tooling, platform capabilities, or developer\-facing products
- Hands\-on experience with RAG (retrieval\-augmented generation) or other grounding techniques for LLMs
- Familiarity with healthcare data formats and standards (FHIR, HL7, claims data, clinical NLP)
- Experience with model evaluation, fairness assessment, or bias detection in ML/AI systems
- Understanding of healthcare regulations (HIPAA, FDA guidance on AI/ML) and responsible AI practices
- Experience establishing QA frameworks, test plans, and quality metrics for ML/AI systems
- Startup or high\-growth environment experience with rapid iteration and learning
- Published research, open\-source contributions, or demonstrated thought leadership in AI/agentic systems
*This job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee. Other duties, responsibilities, and activities may change or be assigned at any time with or without notice.*
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 Vytalize 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 $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.
Vytalize Health AI Hiring
Vytalize Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in KS, 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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