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About This Role
Reporting to the Senior Director of Data \& AI, the Senior Data Architect is a hands\-on technical leader responsible for the architecture, engineering, governance, and optimization of UVVC's enterprise data platform. This role will define technical standards and design scalable data pipelines, medallion data products, semantic models, and governed analytics solutions that enable trusted self\-service analytics and AI\-driven insights across UVVC.
Responsibilities
Data Pipeline Development \& Orchestration
- Design, build, and optimize batch and streaming ETL/ELT pipelines and reusable ingestion frameworks using Azure Data Factory and Databricks across APIs, databases, SaaS platforms, and internal systems.
- Build scalable Delta Lake transformation frameworks using medallion architecture, Spark, and SQL.
- Implement CI/CD, parameterization, triggers, and pipeline automation best practices.
Azure Data Platform Engineering
- Architect, manage, and optimize enterprise data environments across Azure Data Lake Storage Gen2 (ADLS Gen2\), Azure SQL, and Databricks, including serverless and classic compute strategies, cost governance, and workload isolation strategies.
- Implement DataOps practices including testing, version control, monitoring, and documentation.
Unity Catalog, Security \& Data Governance
- Design and administer the enterprise Unity Catalog structure, including catalogs, schemas, external locations, storage credentials, groups, service principals, and ownership models.
- Implement least\-privilege access, governed tags, attribute\-based access\-control policies, row\-level filters, and column\-level masking for PHI, PII, financial, and other sensitive information.
- Establish standards for data classification, lineage, auditability, stewardship, retention, certification, and access reviews.
- Partner with Security, Compliance, Privacy, and business data owners to ensure data solutions align with HIPAA and organizational security requirements.
- Govern tables, views, volumes, functions, metric views, dashboards, models, and Genie Agents through Unity Catalog.
AI/BI, Semantic Models \& Genie Agents
- Design and develop Databricks AI/BI Dashboards and domain\-specific Genie Agents for clinical, operational, financial, RCM, marketing, and executive use cases.
- Configure trusted datasets, joins, business terminology, instructions, example queries, dimensions, measures, synonyms, and approved KPI definitions.
- Develop and govern Unity Catalog metric views and semantic definitions to ensure consistent reporting across Databricks AI/BI and Power BI.
- Establish testing and monitoring processes for Genie Agent accuracy, data grounding, security, explainability, performance, and user adoption.
- Ensure that AI\-generated results respect Unity Catalog permissions and approved business definitions.
Databricks Architecture \& Platform Ownership
- Design and implement enterprise\-grade Databricks Lakehouse Medallion architecture (Bronze, Silver, Gold layers).
- Define and enforce data engineering standards, naming conventions, and architectural patterns across all pipelines.
- Lead the architecture of Delta Lake design patterns, including partitioning, optimization, and data lifecycle management.
- Establish scalable serverless and classic compute strategies, job orchestration frameworks, and workspace organization.
- Evaluate and implement new Databricks capabilities and ensure alignment with enterprise data strategy.
Cross\-Functional Collaboration
- Work closely with clinical, sales, marketing, finance, RCM, operations, HR, and IT teams to understand business needs.
- Provide technical guidance on data engineering patterns and platform capabilities.
- Clearly communicate progress, risks, and technical decisions to data stakeholders and leadership.
Data Modeling and Enterprise Metrics
- Develop conceptual, logical, dimensional, and physical data models supporting clinical, operational, financial, marketing, RCM, and workforce analytics.
- Establish conformed dimensions, master and reference data standards, governed KPIs, and reusable semantic definitions.
- Reduce conflicting calculations and duplicate business logic across Databricks AI/BI, Power BI, and downstream applications.
Data Quality and Observability
- Establish data\-quality rules, reconciliation controls, data SLAs, pipeline observability, alerting, incident response, and root\-cause\-analysis processes.
- Define standards for schema evolution, change data capture, late\-arriving data, historical tracking, retries, and recovery.
Performance and FinOps
- Monitor and optimize Databricks consumption using system tables, workload tagging, budget controls, query profiling, serverless and classic compute selection, SQL warehouse configuration, and data\-layout optimization.
- Establish cost allocation and accountability by environment, data product, department, and workload.
Qualifications
Required Qualifications
- 8\+ years of progressive experience in data engineering, data architecture, analytics engineering, or cloud data platforms, including demonstrated ownership of production Databricks architecture.
- Demonstrated technical leadership and mentoring experience within data engineering or architecture teams.
- Hands\-on expertise with Azure Data Factory, including pipelines, mapping data flows, Integration Runtime configuration and management, triggers, and monitoring.
- Hands\-on expertise with Azure Databricks, including notebooks, Apache Spark, Delta Lake, Databricks SQL, Lakeflow Jobs, Lakeflow pipelines, and workflow orchestration.
- Advanced SQL expertise, including complex transformations, dimensional and semantic modeling, query\-plan analysis, Delta Lake optimization, Databricks SQL, and SQL warehouse performance tuning.
- Advanced proficiency in Python and PySpark for data engineering, reusable framework development, automation, testing, and performance optimization.
- Deep expertise in Medallion Lakehouse architecture (Bronze/Silver/Gold) and Delta Lake optimization techniques.
- Demonstrated experience designing, implementing, and operating enterprise Databricks environments across development, testing, and production, including security, governance, deployment, performance, and cost management responsibilities.
- Strong understanding of Databricks Unity Catalog, data governance, and security models.
- Strong understanding of HIPAA, PHI/PII safeguards, least\-privilege access, data retention, auditability, and secure healthcare data integration.
- Experience defining data platform standards, frameworks, and best practices
Preferred Qualifications
- Experience with AI/ML workflows, feature engineering, or model enablement.
- Experience integrating data across EHR/EMR, CRM, patient\-engagement, contact\-center, marketing, finance/ERP, HRIS, and revenue\-cycle platforms.
- Experience designing enterprise data models and governed KPIs for healthcare operations, including patient volume, referrals, scheduling, conversion, provider productivity, revenue cycle, payer performance, denials, collections, labor, and clinic\-level financial performance.
- Familiarity with real\-time processing (Structured Streaming) within Databricks.
- Experience with master data management, reference data, and entity\-resolution strategies across patients, providers, locations, payers, legal entities, and acquired practices.
About us:
UVVC, is a leading provider of comprehensive vein and vascular care with over 60 clinics across Arizona, Illinois, Colorado, Florida, Georgia, Texas, and expanding. Our mission is to revolutionize vascular care by delivering an all\-inclusive clinic experience that addresses every aspect of lower extremity vein, vascular, and wound conditions.
United Vein \& Vascular Centers (UVVC) is distinguished by its innovative approach to diagnosing and treating a variety of vascular conditions that affect the pelvis and lower extremities. With a team of committed specialists, cutting\-edge medical technology, and a patient\-centric approach that emphasizes minimally invasive procedures, UVVC ensures superior care and optimal outcomes for it's patients.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At United Vein & Vascular Centers, 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 $214,900 based on 6,420 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 $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
United Vein & Vascular Centers AI Hiring
United Vein & Vascular Centers has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tampa, FL, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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