AI Engineer

Vytalize Health

AI Engineer at Vytalize Health focusing on agentic systems and LLM-powered healthcare applications. Collaborate with cross-functional teams to implement AI automation in healthcare workflows.

Posted 7/15/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing LLM-powered applications and agentic systems, ensuring compliance with healthcare regulations and optimizing performance through rigorous evaluation frameworks. Proficient in Python, SQL, and cloud data platforms, with a strong focus on prompt engineering and agent evaluation.

Highest-signal resume keywords

LLM API DevelopmentAgentic System ArchitecturePython ProficiencySQL ProficiencyHealthcare Compliance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringMachine LearningPrompt EngineeringAPI DevelopmentETL/ELT PatternsEvaluation FrameworksQuality AssurancePerformance MetricsDebuggingData Modeling

Soft Skills

Excellent CommunicationAnalytical SkillsMentoring

Tools & Technologies

AWSDatabricksLangChainLangGraphCrewAI

Industry Keywords

HIPAA ComplianceFDA GuidanceAI/ML RegulationsClinical Decision SupportAgentic Systems

Tech Stack

Tools & technologies

AWSCloudETLPythonSQL

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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.

Benefits

Comp & perks

Competitive salary
Flexible working hours
Professional development budget
Home office setup allowance
Global team events