AI Engineering & Enablement Lead

EVERSANA•Overland Park, KS

4h•$178,190 - $213,725•Hybrid

About The Position

EVERSANA is establishing an AI Hub Center of Excellence within Patient Services Technology to revolutionize software development. The AI Engineering & Enablement Lead will spearhead this initiative, focusing on integrating enterprise AI tools into the Patient Services Software Development Life Cycle (SDLC), creating a structured framework for deploying and managing AI agents, implementing engineering best practices, and transforming the current engineering approach into an AI-augmented model. This is a player-coach role, serving as the onshore lead for a hybrid team. The Lead will manage stakeholder engagement, architectural decisions, and governance during US business hours, while an offshore team handles development and testing. The ultimate responsibility is to convert the AI Hub roadmap into deployed, governed, production-ready software, delivered by an AI-accelerated team.

Requirements

8+ years in software engineering, with at least 3 years in a technical lead or architect role.
Proven experience architecting and deploying LLM-based systems or AI agents in production environments, beyond prototypes.
Hands-on proficiency with a major cloud AI platform (Vertex AI preferred; AWS Bedrock or Azure OpenAI acceptable) and leading LLMs (Claude, Gemini, or equivalent).
Working knowledge of agent design patterns: RAG, tool use/function calling, orchestration frameworks (e.g., CrewAI, LangChain, Vertex Agent Builder), and emerging standards like MCP.
Experience implementing AI-assisted development tools (e.g., Claude Code, GitHub Copilot, Cursor) within an engineering organization and driving user adoption.
Familiarity with the Salesforce ecosystem and enterprise integration (e.g., MuleSoft) sufficient to inform architectural decisions.
Strong understanding of governance and compliance for AI in regulated industries, including HIPAA/PHI handling, model risk, and data security.
Excellent executive communication skills, capable of translating technical strategy into business terms for CTO/CFO audiences.
Experience leading distributed onshore and offshore teams.

Nice To Haves

Background in healthcare, life sciences, or pharmaceutical patient services technology.
Previous experience establishing an AI Center of Excellence or a similar enablement function.
Experience with Salesforce Health Cloud, Apex, or LWC.
Vendor management experience with AI or healthcare technology partners.

Responsibilities

Own the AI Hub COE charter and serve as the liaison between EVERSANA's Enterprise AI team and Patient Services engineering.
Integrate and operationalize enterprise AI tooling (GCP, Vertex AI, Claude, Gemini Enterprise) into the daily SDLC of ACTICS (Salesforce Health Cloud), MuleSoft, and Java/.NET teams.
Re-architect the existing engineering practice to an AI-augmented model, standardizing AI-assisted development using tools like Claude Code, Cursor, and GitHub Copilot across Development, Quality Assurance, and Business Analyst functions.
Define and execute a change management strategy to encourage engineers to adopt AI-first workflows.
Architect the framework for agent deployment and lifecycle management on Vertex AI, utilizing Claude and Gemini Enterprise as primary models.
Establish reusable agent patterns, including RAG pipelines, tool/function calling, MCP server integrations, and multi-step orchestration, for teams to build upon.
Set the standards for agent development, evaluation, deployment, monitoring, and retirement in production environments.
Oversee AI governance for Patient Services, including model selection criteria, PHI/HIPAA compliance, evaluation frameworks, and approved tools standards.
Ensure all agents and AI workflows comply with healthcare regulations before production, collaborating with InfoSec for data-flow approval and BAA verification.
Manage the AI risk register and the governance process for the prompt/pattern library.
Lead a hybrid onshore/offshore team in a follow-the-sun model, managing architecture and stakeholder alignment during US hours, with offshore execution overnight.
Plan and execute parallel-track delivery to advance multiple AI MVPs and tech workstreams simultaneously against a tight roadmap.
Define AI velocity Key Performance Indicators (KPIs) such as code-generation rate, defect-rate delta, time-to-merge, and story points per sprint, and report progress and ROI quarterly to the CTO and CFO.
Act as the senior technical authority for AI within Patient Services, engaging with the CTO, CFO, Enterprise AI leadership, and external vendor partners.
Coordinate with adjacent teams (ACTICS, NiCE, MuleSoft integration) and existing product teams to integrate their capacity into AI Hub work.