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About This Role
EEOC Statement
“Lifepoint Health is an Equal Opportunity Employer. Lifepoint Health is committed to Equal Employment Opportunity for all applicants and employees and complies with all applicable laws prohibiting discrimination and harassment in employment.”
You must be authorized to work in the United States without employer sponsorship.
WORK ENVIRONMENT AND TRAVEL REQUIREMENTS:
The position is: Hybrid \- Brentwood, TN
Travel Requirements: Less than 25%
POSITION SUMMARY:
The AI Business Analyst is the strategic bridge between business stakeholders and the AI Transformation engineering team. This role identifies high\-value automation opportunities across the organization, translates business challenges into actionable engineering requirements, and produces executive\-ready communications that demonstrate the team’s impact and ROI.
Leveraging AI tools natively in their own daily workflow, this analyst operates as both a domain expert and an AI power user — helping the organization understand what is possible while ensuring the engineering team builds what actually matters. Candidates who use AI tools daily and demonstrate clear enthusiasm for enterprise AI transformation are strongly preferred over those with a traditional BA background alone.
ESSENTIAL FUNCTIONS:
- Conduct structured discovery sessions with business unit leaders and operational teams to identify manual processes, bottlenecks, and automation opportunities with measurable business impact.
- Translate business requirements into clear, actionable engineering specifications the AI engineering team can build against — including agent scope, data inputs and outputs, success criteria, edge cases, and security considerations.
- Use AI tools natively (including Anthropic Claude, Microsoft Copilot, and other LLMs) to draft executive summaries, business cases, use case briefs, ROI analyses, and presentation materials for senior leadership.
- Maintain and manage the team’s use case pipeline: prioritizing opportunities by business impact and technical feasibility, tracking progress from discovery through deployment, and communicating status to stakeholders.
- Serve as the primary point of contact for business units seeking AI transformation support — qualifying inbound requests, setting appropriate expectations, and managing the engagement model with external teams.
- Develop and maintain standard templates for use case intake, requirements documentation, agent specification, and executive reporting to accelerate the team’s ability to move from idea to build.
- Collaborate with the AI governance team to ensure all proposed use cases align with organizational AI policies, data handling guidelines, responsible AI principles, and compliance requirements.
- Build and maintain stakeholder relationships across business units, IT, and senior leadership — facilitating alignment and removing communication\-driven blockers that slow the team.
- Track business outcomes delivered by deployed AI agents and automation — working alongside the team’s analytical tools to validate that implemented solutions achieve stated ROI.
- Stay current with enterprise AI capabilities, industry use cases, and emerging best practices to proactively surface transformation opportunities to the team and leadership.
QUALIFICATION, EDUCATION, KNOWLEDGE, SKILLS:
The requirements listed below are representative of the knowledge, skills and/or abilities required.
EDUCATION:
Bachelor’s degree in Business Administration, Information Systems, Healthcare Administration, Operations, or related field. Advanced degree a plus but not required.
EXPERIENCE:
3\+ years in a business analyst, management consultant, operations analyst, project manager, or similar role with significant cross\-functional stakeholder engagement. Experience working alongside IT or technology teams is strongly preferred. Demonstrated, hands\-on daily use of AI tools (Claude, ChatGPT, Copilot, or equivalent) may offset traditional experience requirements for exceptional candidates.
KNOWLEDGE, SKILLS \& ABILITIES:
- Demonstrated, hands\-on proficiency using AI tools (Claude, ChatGPT, Microsoft Copilot, or similar) to produce professional\-quality documents, analyses, executive presentations, and communications.
- Exceptional written communication skills — ability to craft compelling, concise executive narratives from complex technical and operational concepts.
- Process analysis and workflow documentation skills: ability to map current\-state business processes, identify friction points and waste, and articulate clear future\-state requirements.
- Familiarity with enterprise platforms such as ServiceNow, Microsoft 365, or ERP systems at a functional user level — sufficient to understand integration potential and data flows.
- Working understanding of AI/ML concepts including large language models, autonomous agents, automation, and data pipelines — sufficient to hold credible conversations with both engineers and executive leaders.
- Understanding of AI security and responsible use considerations: data privacy, access control, appropriate use policies, and the importance of organizational AI governance guardrails.
- Excellent stakeholder management, facilitation, and meeting leadership skills — comfortable leading discovery workshops with both technical and non\-technical audiences.
- Highly organized with the ability to manage a portfolio of parallel initiatives across different business units and stages of maturity.
- Self\-starter mentality — comfortable in a fast\-moving, emerging\-practice environment where the playbook is being written in real time.
- Genuine intellectual curiosity about AI and automation, evidenced by personal experimentation, self\-directed learning, or a clear track record of bringing AI tools into previous work.
CERTIFICATIONS/LICENSURE:
- PMP, CBAP, or Lean Six Sigma Green/Black Belt is a plus. Microsoft AI Fundamentals (AI\-900\) or similar AI literacy certification is desirable and supported. An appetite for continuous learning in AI is essential.
We employ and provide care to people from all walks of life. We are committed to promoting healing, providing hope, preserving dignity and producing value with an inclusive workforce in which diversity is leveraged, respected, and reflective of the patients, family members, customers and team members we serve.Lifepoint Health is a leader in community\-based care and driven by a mission of Making Communities Healthier. Our diversified healthcare delivery network spans 29 states and includes 63 community hospital campuses, 32 rehabilitation and behavioral health hospitals, and more than 170 additional sites of care across the healthcare continuum, such as acute rehabilitation units, outpatient centers and post\-acute care facilities. We believe that success is achieved through talented people. We want to create places where employees want to work, with opportunities to pursue meaningful and satisfying careers that truly make a difference in communities across the country.
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 Lifepoint Health, 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.
Lifepoint Health AI Hiring
Lifepoint Health has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Positions span US, Brentwood, TN, US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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