Senior Engineer, AI Agentic & Transformation

Lifepoint Health•Brentwood, TN

8d•Remote

About The Position

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.

Requirements

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.
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.

Nice To Haves

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.

Responsibilities

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