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About This Role
About MindCare Solutions
MindCare Solutions is a premier provider of behavioral health services, supporting hospitals, emergency departments, inpatient behavioral health units, long\-term care centers, correctional settings and various other clinical settings.
We partner with healthcare organizations to deliver top\-tier, end\-to\-end behavioral health solutions, integrating evidence\-based care pathways, advanced technology, and exceptional providers. Our organization is committed to enhancing mental health care accessibility and efficacy through innovative solutions and strong partnerships, ensuring that high\-quality, patient\-centered care is delivered to improve the health and well\-being of the communities we serve.
Senior Data Platform \& Healthcare AI Analytics Engineer
Location: Remote
Compensation: negotiable
Position Overview
We are seeking a Senior Data Platform \& Healthcare AI Analytics Engineer to architect, build, and evolve an AI\-augmented healthcare data and analytics ecosystem.
This is a high\-impact full\-stack data platform role for a technical builder who understands that behind every data point is a patient. Responsible for designing the pipelines, platforms, and analytics models that transform fragmented healthcare data into a scalable intelligence layer supporting analytics, operational decision\-making, and AI\-driven insights.
Creating an AI\-first architecture leveraging Microsoft Fabric, Azure AI Services, third\-party AI tools, (ChatGPT, Claude), Azure Synapse, Azure Data Factory, Azure SQL, and Power BI, this role will integrate clinical, financial, and operational data sources to create a unified Healthcare Intelligence platform. The platform will support improved patient outcomes, operational efficiency, revenue cycle performance, and compliance with HIPAA and healthcare regulatory standards.
Beyond building infrastructure, this role is expected to enable business insights for the performance deep analytical work to identify trends, inefficiencies, and revenue opportunities, translating complex healthcare data into actionable insights that improve how the organization delivers care.
Core Responsibilities
Data Platform Architecture \& Engineering
- Architect and operate the organization’s Azure / Microsoft Fabric healthcare data platform
- Design scalable ETL/ELT pipelines integrating EMR/EHR systems via HL7, FHIR APIs, and direct integrations
- Design and develop secure, scalable EMR interface APIs enabling bi\-directional interoperability between Microsoft Dynamics 365 Health Cloud and partner EMRs, supporting clinical workflows (orders, documentation, patient context, care coordination) using HL7, FHIR, and modern API\-based integration patterns.
- Build, extend and maintain the lakehouse and warehouse environments using Microsoft Fabric, Azure Synapse, and Azure Data Lake
- Integrate healthcare data from EMRs, revenue cycle systems, telehealth platforms, scheduling systems, and external healthcare datasets
- Ensure reliability, scalability, and performance of the enterprise data platform
Analytics Platform \& Power BI Architecture
- Design and manage the Power BI analytics platform, including governance, workspaces, and enterprise datasets
- Build Power BI semantic models, reusable datasets, and reporting frameworks
- Deliver executive and operational dashboards supporting clinical, operational, and financial leaders
- Enable self\-service analytics across the organization
Healthcare Analytics \& Business Intelligence
Analyze healthcare data to generate insights across core operational domains, including:
- provider productivity and utilization
- patient access and scheduling performance
- encounter lifecycle and clinical workflow efficiency
- revenue cycle and claims performance
- payer reimbursement and financial trends
Translate complex healthcare datasets into clear insights that improve patient outcomes, operational performance, and financial sustainability.
AI\-Enabled Healthcare Analytics
Design and implement the data foundation for AI\-driven healthcare analytics, including:
- predictive models for patient no\-shows, provider capacity, and care demand
- analytics to detect claims denials and revenue leakage
- AI\-assisted analytics using Azure Machine Learning, Fabric AI capabilities, and Azure OpenAI
- enabling predictive and AI\-driven insights within Power BI and analytics platforms
The goal is to move the organization from reactive reporting to predictive and intelligent analytics.
Technology Stack
This role will architect and operate the organization’s healthcare data and AI platform built on:
Data Platform
- Microsoft Fabric
- Azure Synapse
- Azure Data Factory
- Azure SQL
- Azure Data Lake
API \& Integration Architecture
- RESTful APIs / JSON / OAuth 2\.0
- GraphQL (optional but valuable)
- Azure API Management
- Azure Functions / serverless
- Event\-driven architecture (Event Hub, Service Bus)
Analytics
- Power BI
- Semantic data models
- DAX and enterprise reporting frameworks
- Agentic AI Tools
Engineering / Development Stack
- SQL / T\-SQL
- Python
- Spark / PySpark
- ETL / ELT pipelines
- C\# /.Net
- Azure DevOps / Github Actions
- Postman / Swagger / OpenAPI
AI / Machine Learning \& Agentic AI
- Azure Machine Learning
- Microsoft Fabric AI capabilities
- Azure OpenAI
- Python ML frameworks
- Healthcare AI (ChatGPT, Claude for Healthcare)
- Agentic AI (LLM integration, RAG (Retrieval\-Augmented Generation, Prompt engineering, multi\-agent systems / workflow orchestration
Healthcare Interoperability
- Microsoft Dynamics 365 – Dataverse / Microsoft Cloud for Healthcare
- HL7 (Especially ORM, OPRU, ADT, and MDM)
- FHIR APIs
- CCD / Clinical document exchange
- EMR/EHR integrations (e.g., Epic, Meditech, Cerner, Athena, and a multitude of Behavioral Health EMRs)
Governance, Security \& Compliance
- Implement and maintain HIPAA\-compliant data architecture protecting Protected Health Information (PHI).
- Deploy security practices including row\-level security (RLS), data masking, encryption, and role\-based access controls.
- Establish data quality monitoring and validation frameworks to ensure reliable clinical and operational reporting.
- Maintain detailed data lineage, documentation, and audit trails to support regulatory reporting (MIPS, MACRA, CMS quality programs).
Qualifications
- 7\+ years’ experience in Data Engineering, Analytics Engineering, or Data Platform roles
- 3\+ years’ experience working with healthcare data, including EMR/EHR and/or revenue cycle datasets
- Advanced expertise with the Microsoft Azure data platform, including:
- + Azure Synapse
+ Azure SQL
+ Azure Data Factory
+ Microsoft Fabric
- Proven experience building enterprise Power BI platforms, including semantic models, datasets, governance, and dashboards
- Advanced proficiency in SQL (T\-SQL) and experience with Python or Spark for data engineering, transformation, and analytics
- Strong experience designing scalable data pipelines and data models supporting AI, machine learning, and advanced analytics workloads
- Experience enabling AI\-driven analytics, including preparation of datasets for predictive modeling and integration of AI insights into reporting platforms
- Experience designing or supporting API\-driven and event\-based architectures, including REST APIs and modern integration patterns
- Experience working with healthcare interoperability standards, including:
- + HL7
+ FHIR APIs
- Ability to design systems supporting clinical workflows and data exchange across EMR systems
- Strong analytical mindset with ability to generate operational and financial insights from healthcare data
- Deep understanding of HIPAA, HITECH, and PHI data protection requirements
- Experience with behavioral health, telehealth, or outpatient healthcare organizations
- Experience designing and implementing bi\-directional EMR integrations, including API\-based interoperability between platforms such as Microsoft Dynamics 365 Health Cloud and partner EMRs
- Experience with healthcare data models, including OMOP or similar frameworks
- Experience building predictive analytics or AI\-driven healthcare models (e.g., access, utilization, RCM optimization)
- Experience with agentic AI systems, including:
- + AI orchestration frameworks
+ LLM integration (Azure OpenAI, ChatGPT, Claude)
+ Retrieval\-Augmented Generation (RAG)
+ AI\-driven workflow automation
- Experience integrating AI capabilities into analytics platforms (e.g., AI\-assisted dashboards, automated insights)
- Experience supporting Revenue Cycle Management (RCM) analytics, including:
- + claims lifecycle
+ denial management
+ reimbursement optimization
- Experience analyzing payer mix, reimbursement trends, and claims data
- Experience with Azure Machine Learning or similar ML platforms
- Microsoft certifications such as:
- + Azure Data Engineer Associate
+ Power BI Data Analyst Associate
Benefits:
- Full health and wellness (Medical, Dental, Vision)
- Flexible spending account
- 401K with 4% match
- Company paid life insurance
- Voluntary life/AD\&D, short/Long term disability
- Positive work environment/culture
- Company paid holidays
- PTO
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At MindCare Solutions Group, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
MindCare Solutions Group AI Hiring
MindCare Solutions Group has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Nashville, TN, US.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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