Note: The job is a remote job and is open to candidates in USA. EVERSANA is a global life sciences commercialization services company focused on bringing innovative therapies to market and supporting patients. The AI Engineering & Enablement Lead will establish and operate an AI Center of Excellence within Patient Services Technology, productionize enterprise AI tools and agents, and transform software development into an AI-augmented practice. The role leads a hybrid team, owns AI architecture and governance, and drives delivery, compliance, stakeholder alignment, and adoption.
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
Skills
- • 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
- • 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
- • Travel (Minimal)
- • Hours (40 hours, Monday through Friday)
- Office: While performing the essential functions of this job the employee is frequently required to reach, grasp, stand and/or sit for long periods of time (up to 90% of the shift), walk, talk and hear; occasionally required to lift and/or move up to 25 pounds
- • 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)
- • 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
Company Overview
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