Engineer, Backend Integration & AI Transformation

Lifepoint Health•Brentwood, TN

1d•Remote

About The Position

The Backend Integration Engineer is the connective tissue of the AI Transformation team. This engineer builds and maintains the API infrastructure and system integrations that allow AI agents to interact with the organization’s core enterprise platforms — including ServiceNow, Okta, ERP systems, and other business-critical tools. This role ensures that when an AI agent needs to take action in the real world, the pathways are secure, reliable, well-documented, and reusable. This is a role for a solid backend developer who is excited to apply strong fundamentals in an AI-first context. Experience with enterprise platform APIs is a strong differentiator. Direct MCP server development experience is rare and not required — a willingness to become an internal expert is expected.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or related field preferred. Equivalent practical experience will be considered.
2–4 years of backend software development or systems integration experience.
Proficiency in at least one backend language — Python strongly preferred; Node.js or Java acceptable.
Strong understanding of REST API design principles, authentication and authorization patterns (OAuth 2.0, OIDC, API keys, JWT), and API gateway configuration.
Experience consuming and integrating with enterprise platform APIs (ServiceNow, Okta, Microsoft Graph, SAP, or similar).
Knowledge of Model Context Protocol (MCP) or ability and enthusiasm to rapidly learn and implement MCP server patterns — a key skill for this team.
Security-first development mindset: input validation and sanitization, secure credential handling and rotation, least-privilege API scoping, audit logging, and OWASP API Security Top 10 awareness.
Familiarity with Azure API Management, Azure Functions, Azure Service Bus, or similar Azure integration and serverless platform services.
Understanding of event-driven architecture, webhook patterns, and asynchronous integration approaches.
Experience with version control (Git), code review practices, and collaborative development workflows.
Ability to write clear, accurate API documentation and integration specifications that other engineers can build on.

Nice To Haves

Experience with enterprise platform APIs (ServiceNow, Okta, Microsoft 365, or ERP systems) is a strong plus.
Candidates with strong API and backend fundamentals who demonstrate genuine enthusiasm for AI-driven automation will be considered.
Genuine curiosity about AI agents and automation — understanding of how MCP tools are invoked by LLMs is a significant plus.
Microsoft Azure Developer Associate (AZ-204) preferred.
ServiceNow Certified System Administrator (CSA) or Okta certifications are a strong plus.
OWASP API Security awareness desirable.

Responsibilities

Design, build, and maintain RESTful and event-driven APIs that expose enterprise system capabilities to AI agents and agentic workflows in a secure and controlled manner.
Develop and maintain Model Context Protocol (MCP) server implementations that provide AI agents with structured, secure, and auditable access to enterprise tools, data sources, and services.
Build and manage integrations with key enterprise platforms including ServiceNow (workflow triggers, CMDB queries, ticket management), Okta (identity and access queries, group management), and ERP/financial systems.
Implement security controls for all integration endpoints: OAuth 2.0 / OIDC authentication, API gateway policies, rate limiting, input sanitization, payload validation, and comprehensive audit logging.
Collaborate with Agentic AI Engineers to define MCP tool specifications and ensure agents can reliably invoke enterprise actions with appropriate authorization checks and operational guardrails.
Maintain comprehensive API documentation and an integration catalog so the team can build on existing ‘paved paths’ rather than re-implementing integrations from scratch.
Monitor integration health, SLA adherence, and error rates; implement alerting and graceful degradation patterns so agent failures are isolated and recoverable.
Work with the AI governance team to ensure integration designs comply with data classification policies, access control requirements, and applicable regulatory standards.
Evaluate and onboard new enterprise system integrations as the team’s scope of automation expands to additional business units and platforms.
Contribute to the team’s security review process for agent tool definitions, ensuring tool permissions follow the principle of least privilege.