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About This Role
Our Company:
BrightSpring Health Services
Overview:
We are seeking a highly experienced and hands\-on Principal Enterprise Architect to lead the full stack design and integration of advanced AI/ML platform and solution architectures within our AI\-enabled Health Cloud Platform. This role combines deep expertise in end to end systems architecture, platform engineering, and integration/API design, enabling seamless, secure, and scalable AI/ML healthcare applications.
You will play a critical role in shaping both the technical foundation of the platform and the delivery of intelligent healthcare solutions, ensuring that AI capabilities such as Generative AI, Agentic AI, RPA, and fine\-tuned RAG/Vector DB models are effectively embedded into clinical and pharmacy workflows.
Responsibilities:
- Architect and lead the full stack implementation of end\-to\-end AI/ML platform and solution architectures, ensuring seamless integration with enterprise systems and healthcare data sources.
- Design and develop robust, scalable APIs and microservices using technologies such as .NET, C\#, and RESTful services.
- Define and enforce platform and integration architecture standards, including data contracts, API security, service orchestration, and interoperability.
- Collaborate with AI/ML engineers, product managers, API, and data engineers to translate business and clinical requirements into cloud\-native, reusable platform capabilities and solution\-specific workflows.
- Architect and optimize LLM\-based systems, including RAG pipelines, vector databases, and agentic AI frameworks (e.g., LangChain, AutoGen).
- Design and evolve the AI/ML platform architecture, including model lifecycle management, orchestration layers, and reusable AI services.
- Ensure all solutions and platform components are HIPAA\-compliant, secure, and aligned with healthcare interoperability standards (FHIR, HL7 etc.).
- Evaluate and integrate third\-party AI services, open\-source tools, and cloud\-native components into the platform.
- Provide architectural leadership across model training, deployment, monitoring, and retraining, ensuring scalability and performance.
Qualifications:
- 10\+ years of experience in solution and platform architecture, with a strong focus on AI/ML systems and enterprise integration.
- 15\+ years in overall solution architecture.
- Proven experience designing and delivering AI\-powered healthcare or pharmacy platforms and solutions.
- Experience with multi\-agent systems, RPA tools, and intelligent automation in healthcare.
- Strong experience with API development and integration using .NET, C\#, and modern API frameworks.
- Deep understanding of cloud platforms (Azure preferred), containerization (Docker, Kubernetes), and MLOps practices.
- Expertise in Generative AI, RAG architectures, vector databases, and agentic AI orchestration.
- Expertise in healthcare data standards and secure data handling practices.
- Excellent communication and stakeholder engagement skills.
Preferred Qualifications:* Background in AI/ML engineering, data science, or biomedical informatics.
- Knowledge of responsible AI, model explainability, and bias mitigation.
- Advanced degree (MS or PhD) in Computer Science, AI/ML, or a related field.
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. Additional Job Information:
Why Join Us?* Architect the future of AI in healthcare by shaping both platform capabilities and solution delivery.
- Collaborate with a forward\-thinking team of engineers, clinicians, and product leaders.
- Enjoy a highly competitive compensation and benefits package.
- Be part of a mission\-driven organization focused on transforming healthcare through intelligent technology.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
BrightSpring Health Services AI Hiring
BrightSpring Health Services has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Louisville, KY, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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