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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: Remote
Travel Requirements: Less than 25%
POSITION SUMMARY:
The Senior Agentic AI Engineer is a hands\-on builder responsible for designing, developing, and deploying AI\-powered autonomous workflows and agentic systems that eliminate manual processes across the organization.
The role requires an individual who is self\-driven, intellectually curious, and able to work independently in a rapidly evolving technical environment. Candidates should demonstrate strong foundational software engineering skills, the ability to learn new tools and concepts through self\-directed study, and an aptitude for applying emerging AI technologies to practical business problems. While direct production experience with all listed technologies is preferred, it is not required if the candidate can show relevant technical ability, sound problem\-solving skills, and a proven capacity to quickly build knowledge in new areas.
ESSENTIAL FUNCTIONS:
- Design and build LLM\-powered agentic workflows using Azure AI Foundry and Anthropic Claude APIs that automate business processes currently requiring manual human effort.
- Develop multi\-step AI agent pipelines using orchestration frameworks (e.g., LangChain, AutoGen, Semantic Kernel, or custom implementations) integrated with enterprise systems such as ServiceNow, Okta, and ERP platforms.
- Implement Model Context Protocol (MCP) integrations to enable AI agents to securely access and act on enterprise data sources, tools, and APIs — becoming an internal subject\-matter expert on MCP patterns and best practices.
- Apply security\-by\-design principles throughout all AI agent development: prompt injection defense, least\-privilege tool access, secrets management, output validation, and safe handling of sensitive organizational data.
- Build, test, and iterate on prompts, system instructions, and agent reasoning chains to ensure reliable, accurate, and safe agent behavior across diverse business scenarios.
- Partner with the Backend Integration Engineer to connect agent workflows to existing enterprise API infrastructure and MCP server endpoints.
- Collaborate with the AI Business Analyst to translate business use cases into technical agent requirements and working prototypes.
- Participate in code reviews, maintain internal documentation, and contribute to the team’s shared engineering standards for agentic AI development.
- Stay current with rapidly evolving AI tooling, model capabilities, and agentic design patterns — proactively experimenting and bringing new techniques to the team.
- Assist in evaluating AI agent outputs for accuracy, safety, and alignment with intended business outcomes before production deployment.
- Designing multi\-step autonomous workflows
- Integrating AI with enterprise tools and APIs
- Prompt engineering, orchestration, tool calling, RAG, and agent safety
- Turning business use cases into working AI solutions
- Ensuring AI systems behave reliably, securely, and accurately
QUALIFICATION, EDUCATION, KNOWLEDGE, SKILLS:
The requirements listed below are representative of the knowledge, skills and/or abilities required.
EDUCATION:
Bachelor’s degree in computer science, Information Systems, Software Engineering, or related field preferred. Equivalent practical experience or a strong portfolio of AI projects will be considered.
EXPERIENCE:
4\+ years of experience in a foundational IT, software engineering, systems integration, or application development environment required, including at least 2\+ years of hands\-on experience working with AI, automation, orchestration, or integration platforms such as Azure AI Foundry, Anthropic Claude APIs, LangChain, AutoGen, Semantic Kernel, ServiceNow, Okta, ERP\-connected workflows, or similar enterprise automation technologies.
Candidates should demonstrate practical experience building, supporting, or enhancing technical solutions in production or pilot environments, along with strong core competencies in programming, API integration, troubleshooting, and systems thinking. Experience with agentic AI projects, whether in professional settings or through substantial independent work, is strongly preferred.
KNOWLEDGE, SKILLS \& ABILITIES:
- Working knowledge of Python and REST API consumption and integration.
- Familiarity with Azure cloud services; Azure AI Foundry experience is a strong plus.
- Understanding of LLM concepts: prompting, tool use / function calling, context management, retrieval\-augmented generation (RAG), and multi\-agent orchestration.
- Exposure to or eagerness to deeply learn Model Context Protocol (MCP) — the emerging standard for connecting AI agents to enterprise tools and data sources.
- AI security awareness: prompt injection attacks, data leakage risks, insecure tool execution, jailbreaking techniques, and general OWASP software security principles.
- Experience with version control (Git) and basic software development lifecycle (SDLC) practices.
- Intellectual curiosity and a demonstrated self\-directed learning habit — personal projects, open\-source contributions, online courses, or similar evidence welcomed.
- Strong problem\-solving skills and ability to operate independently in a fast\-moving, ambiguous environment.
- Excellent written and verbal communication skills — able to explain technical concepts to non\-technical stakeholders.
CERTIFICATIONS/LICENSURE:
Microsoft Azure AI Engineer Associate (AI\-102\) or Azure Fundamentals (AZ\-900\) preferred. Willingness to pursue relevant AI and Azure certifications is expected and supported by the organization.
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 Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 3,708 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Lifepoint Health, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $238,500 based on 58 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
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 Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
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).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
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.
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