AI Engineering & Enablement Lead

EVERSANA is standing up an AI Hub Center of Excellence within Patient Services Technology to transform how we build and deliver software. The AI Engineering & Enablement Lead owns this mission: to ingest enterprise AI tooling and productionize it into the Patient Services SDLC, build a governed framework for deploying and maintaining AI agents, establish engineering best practices, and re-architect our current engineering practice into an AI-augmented software development organization. This is a player-coach role. The Lead is the onshore anchor of a hybrid team — running stakeholder alignment, architecture decisions, and governance during US hours while an offshore team executes build and test. The Lead is accountable for turning the AI Hub roadmap from a backlog of capabilities into shipped, governed, production-grade software delivered by an AI-accelerated team.

Responsibilities

Own the AI Hub COE charter and act as the bridge between EVERSANA's Enterprise AI team and Patient Services engineering; Ingest and operationalize enterprise AI tooling (GCP, Vertex AI, Claude, Gemini Enterprise) into the day-to-day SDLC of the ACTICS (Salesforce Health Cloud), MuleSoft, and Java/.NET teams; Re-architect existing engineering practice into an AI-augmented model — standardizing AI-assisted development with Claude Code, Cursor, and GitHub Copilot across Dev, QA, and BA functions; Define and drive the change-management path so engineers adopt AI-first workflows, not just have access to the tools; Architect the agent deployment and lifecycle framework on Vertex AI, with Claude and Gemini Enterprise as primary models; Establish reusable agent patterns — RAG pipelines, tool/function calling, MCP server integrations, multi-step orchestration — that teams can build on; Set the standard for how agents are built, evaluated, deployed, monitored, and retired in production; Own AI governance for Patient Services: model selection criteria, PHI/HIPAA handling, evaluation frameworks, and the approved-tools standard; Ensure every agent and AI workflow meets healthcare compliance requirements before production, coordinating with InfoSec on data-flow approval and BAA verification; Maintain the AI risk register and the prompt/pattern library governance process; Lead a hybrid onshore/offshore team on a follow-the-sun model — architecture and stakeholder alignment during US hours, offshore execution overnight, delivered to a ready queue each morning; Plan and run parallel-track delivery so multiple AI MVPs and tech workstreams progress simultaneously against a compressed roadmap; Define AI velocity KPIs (code-generation rate, defect-rate delta, time-to-merge, story points per sprint) and report progress and ROI quarterly to the CTO and CFO; Serve as the senior technical voice for AI in Patient Services with the CTO, CFO, Enterprise AI leadership, and external vendor partners; Coordinate with adjacent pods (ACTICS, NiCE, MuleSoft integration) and existing product teams as their capacity flows into AI Hub work.

Requirements

8+ years in software engineering, with 3+ years in a technical lead or architect capacity; Demonstrated experience architecting and deploying LLM-based systems or AI agents in production — not just prototypes; Hands-on fluency with a major cloud AI platform (Vertex AI strongly preferred; AWS Bedrock or Azure OpenAI acceptable) and with leading LLMs (Claude, Gemini, or equivalent); Working knowledge of agent design patterns: RAG, tool use / function calling, orchestration frameworks (CrewAI, LangChain, or Vertex Agent Builder), and emerging standards such as MCP; Experience introducing AI-assisted development tooling (Claude Code, GitHub Copilot, Cursor, or similar) into an engineering organization and driving adoption; Familiarity with the Salesforce ecosystem and enterprise integration (MuleSoft or comparable) sufficient to guide architecture decisions; Strong grasp of governance and compliance for AI in a regulated environment — HIPAA/PHI handling, model risk, and data security; Excellent executive communication; able to translate technical strategy into business terms for CTO/CFO audiences; Experience leading distributed onshore/offshore teams.

Nice to have

Background in healthcare, life sciences, or pharmaceutical patient services technology; Prior experience standing up an AI Center of Excellence or similar enablement function; Salesforce Health Cloud, Apex, or LWC experience; Vendor-management experience with AI or healthcare-technology partners.