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About This Role
Our Company:
BrightSpring Health Services
Overview:
We are seeking a highly experienced Enterprise Architect to lead the end\-to\-end architecture of our AI\-enabled Health Cloud Platform, supporting clinical, pharmacy, and operational workflows.
This role is responsible for defining and governing enterprise architecture across cloud, data, AI, and application layers, driving scalable, secure, and compliant solutions leveraging Azure, Databricks, open\-source technologies, and modern application frameworks.
You will collaborate with cross\-functional teams to architect next\-generation AI platforms, including Generative AI, Agentic AI, RAG, intelligent automation, and Computer Using Agents, ensuring alignment with healthcare interoperability and compliance standards.
Responsibilities:
- Define and lead enterprise architecture strategy for AI Health Cloud across compute, storage, network, security, data, and application layers.
- Architect end\-to\-end cloud\-native solutions on Azure, integrating Databricks, AI/ML services, and open\-source frameworks.
- Design and implement intelligent automation solutions leveraging RPA (Automation Anywhere / UiPath), Generative AI, and agentic frameworks.
- Architect scalable AI/ML and GenAI pipelines, including RAG, vector databases, and agent\-based systems.
- Establish architecture patterns for event\-driven systems, including eventing, pub\-sub, queuing, and streaming architectures.
- Lead application architecture using .NET, C\#, and microservices patterns, ensuring high performance and resiliency.
- Define API\-first and interoperability frameworks, enabling integration across clinical and pharmacy platforms (FHIR, HL7, APIs).
- Architect secure, compliant solutions aligned to HIPAA, enterprise security, identity, encryption, and governance standards.
- Partner with platform teams to design and scale Databricks lakehouse architectures (Medallion: Bronze/Silver/Gold).
- Govern architecture decisions, design standards, and reusable frameworks across OLTP and analytics applications, ensuring enterprise\-wide consistency, scalability, performance, and seamless integration between transactional and analytical ecosystems.
- Lead evaluation and integration of RPA/CUA tools, APIs, orchestration platforms, and emerging technologies.
- Mentor engineering teams and drive a culture of architectural rigor, innovation, and engineering excellence.
- Document architecture decisions, frameworks, and operational playbooks.
Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
- 10\+ years of experience architecting end\-to\-end enterprise cloud solutions.
- Deep expertise in Azure cloud architecture (compute, storage, networking, and security).
- Strong experience with Databricks and modern data platforms (lakehouse architecture, data pipelines).
- Proficiency in .NET / C\# application architecture and microservices\-based design.
- Hands\-on experience with open\-source technologies and AI frameworks (e.g., LangChain, MLflow, Hugging Face).
- Strong understanding of distributed systems, including:
- + Event\-driven architecture
+ Pub\-sub and messaging systems
+ Queuing and streaming frameworks
+ API gateway and integration patterns
- Experience designing secure, HIPAA\-compliant healthcare platforms, including data governance.
- Knowledge of interoperability standards (FHIR, HL7, REST APIs).
- Proven experience delivering large\-scale, mission\-critical platforms in regulated environments.
- Strong communication skills with the ability to influence executive stakeholders and cross\-functional teams.
- Experience with AI/GenAI platforms, including RAG, vector search, and agentic systems.
- Experience with MLOps and AI lifecycle management (MLflow, CI/CD, model governance).
- Familiarity with containers and orchestration (Docker, Kubernetes, AKS).
- Experience with multi\-cloud or hybrid cloud architectures.
Preferred Qualifications* Background in clinical, pharmacy, or PBM systems.
- Contributions to open\-source projects or architecture communities.
About our Line of Business: BrightSpring Health Services provides complementary home\- and community\-based health solutions for complex populations in need of specialized and/or chronic care. Through the Company’s service lines, including pharmacy, home health care, and rehabilitation, we provide comprehensive and more integrated care and clinical solutions in all 50 states to over 475,000 customers, clients and patients daily. BrightSpring has consistently demonstrated strong and industry\-leading quality metrics across its services lines, while improving the health and quality of life for high\-need individuals and reducing overall healthcare system costs. For more information, please visit www.brightspringhealth.com. Follow us on Facebook, LinkedIn, and X.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At BrightSpring Health Services, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
BrightSpring Health Services AI Hiring
BrightSpring Health Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Louisville, KY, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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