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About This Role
Join Arizona’s largest, most prominent not-for-profit hospice, serving the valley since 1977.
Hospice of the Valley is a national leader in hospice care and has been serving the Phoenix metropolitan area since 1977. A mission-driven, not-for-profit organization, Hospice of the Valley employs compassionate, skilled professionals who are committed to excellence, enjoy teamwork, and contribute daily to our mission and culture of caring. Team members experience a friendly, supportive atmosphere, leadership support, autonomy, flexibility, and the privilege of doing meaningful, rewarding work.
Benefits:
- Supportive work environment with a culture of caring for patients and one another.
- Competitive wages and excellent benefit program.
- Generous Paid Time Off.
- Flexible schedules for work/life balance.
Position Profile
The Senior Data & AI Engineer will need to have deep handson experience in Snowflake, Microsoft Fabric (incl. OneLake), and healthcare data ecosystems. The ideal candidate understands data modeling, data integration, and data transformation across structured and unstructured sources, and can build machine learning pipelines that operate on claims and clinical data. You’ll design secure, scalable data platforms; map and normalize data across payers, providers, CMS datasets, EHR systems, and HIEs; and operationalize AI tools to drive measurable outcomes in cost, quality, and member/patient experience.
Responsibilities
Data Platform Engineering
- Architect, implement, and optimize data solutions in Snowflake and Microsoft Fabric (incl. OneLake, Lakehouses, Warehouses, and Data Engineering pipelines).
- Build robust ingestion frameworks for batch and streaming data (e.g., ADLS, EventHub, APIs, SFTP) with lineage and governance.
- Manage data security, privacy, and compliance (HIPAA/PHI; rolebased access, masking, tokenization, deidentification).
Data Modeling & Integration
- Design conceptual/logical/physical models (normalized, dimensional/star, data vault where appropriate).
- Implement data mapping and transformations for structured (claims, eligibility, provider, enrollment) and unstructured (clinical notes, PDFs) data.
- Harmonize healthcare data using FHIR/HL7/CCDA, X12/EDI 837/835, NCPDP, and CMS standards; reconcile and link records across EHR and HIE sources.
Analytics & Machine Learning
- Build ML pipelines for risk stratification, cost/utilization forecasting, fraud/waste/abuse detection, quality measure computation (e.g., HEDIS), and care gap identification.
- Operationalize models with MLOps (experiment tracking, reproducibility, CI/CD, monitoring, drift detection).
- Leverage LLMs/AI tools for data quality, entity resolution, summarization, and clinical insights—ensuring safety, bias checks, and auditability.
Governance & Observability
- Implement data cataloging, lineage, and metadata (e.g., Microsoft Purview or equivalent).
- Establish quality SLAs, validation rules, profiling, and automated anomaly detection.
- Instrument pipelines for cost, performance, and reliability (e.g., Snowflake resource monitors, Fabric capacities).
Collaboration & Delivery
- Work with product owners, clinicians, actuaries, and analytics teams to translate requirements into scalable solutions.
- Produce clear documentation, data dictionaries, and mapping specs; mentor engineers and analysts.
- Contribute to architectural roadmaps, reference patterns, and best practices across the enterprise.
Maintains and enhances professional skills.
Adheres to high standards of personal and professional conduct.
Minimum Qualifications
- 8+ years in data engineering/analytics; 5+ years handson with Snowflake (compute, storage, virtual warehouses, tasks, streams, Snowpipe, Time Travel, RBAC, row/column masking, data sharing, Dynamic Tables).
- 2+ years with Microsoft Fabric (including OneLake, Lakehouses, Warehouses, Dataflows Gen2, Notebooks, Pipelines; capacity management).
- Strong data modeling expertise (dimensional/star, 3NF, data vault; surrogate keys, SCD types, conformed dimensions).
- Data integration & transformation proficiency: SQL (advanced), dbt or Fabric Dataflows/Power Query M, ADF/Synapse/Fabric Pipelines, Python for ETL/ELT.
- Mapping from CMS data (e.g., Medicare datasets, claims/encounters), X12/EDI, FHIR/HL7, provider and eligibility.
- Experience with structured (tables, CSV, Parquet) and unstructured (clinical notes, PDFs, blobs) data; NLP pipelines (optional but valued).
- Machine learning: feature engineering, model training/evaluation, and deployment (e.g., scikitlearn, PyTorch/TensorFlow, Fabric ML/Notebook, Azure ML); production monitoring.
- Security & compliance: HIPAA, PHI handling, auditing, data residency, BAAs; practical access control in Snowflake/Fabric.
- Strong communication; ability to author mapping specs, lineage docs, and present tradeoffs to technical and nontechnical stakeholders.
Preferred Qualifications
- Interoperability: FHIR R4, HL7 v2, X12/EDI (837/835), NCPDP; experience with HIEs and EHR integrations (Epic, Cerner, etc.).
- CMS & payer/provider data: Medicare feeforservice, MA, Medicaid, CCW, APCD, and quality programs; risk adjustment (HCC), HEDIS measures.
- MLOps & DevOps: MLflow, DVC, GitHub Actions/Azure DevOps, containerization (Docker), orchestration (Airflow, Fabric Pipelines, or ADF).
- Governance: Microsoft Purview (catalog, lineage, classifications), data quality tools.
- Visualization: Power BI and Fabric Direct Lake; semantic modeling and rowlevel security.
- Cloud: Azure (ADLS, Event Hub, Functions, Key Vault, Databricks), optional AWS/GCP exposure.
- Certifications: Snowflake SnowPro Core/Advanced, Microsoft Certified (Azure Data Engineer Associate, Fabric Analytics Engineer).
Hospice of the Valley is an equal employment opportunity employer. EOE/M/F/D/V
Location: Hospice of the Valley · Information Services
Schedule: Full Time 40 hrs/week, Day Shift, 8am - 5pm
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 Hospice of the Valley (AZ), 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. Senior-level AI roles across all categories have a median of $228,700.
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.
Hospice of the Valley (AZ) AI Hiring
Hospice of the Valley (AZ) has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Phoenix, AZ, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 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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