AI Engineering & Enablement Lead

At EVERSANA, we are proud to be certified as a Great Place to Work across the globe. We’re fueled by our vision to create a healthier world. How? Our global team of more than 7,000 employees is committed to creating and delivering next-generation commercialization services to the life sciences industry. We are grounded in our cultural beliefs and serve more than 650 clients ranging from innovative biotech start-ups to established pharmaceutical companies. Our products, services and solutions help bring innovative therapies to market and support the patients who depend on them. Our jobs, skills and talents are unique, but together we make an impact every day. Join us!  Across our growing organization, we embrace diversity in backgrounds and experiences. Improving patient lives around the world is a priority, and we need people from all backgrounds and swaths of life to help build the future of the healthcare and the life sciences industry. We believe our people make all the difference in cultivating an inclusive culture that embraces our cultural beliefs.  We are deliberate and self-reflective about the kind of team and culture we are building. We look for team members that are not only strong in their own aptitudes but also who care deeply about EVERSANA, our people, clients and most importantly, the patients we serve.   We are EVERSANA.   THE POSITION : 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. ESSENTIAL DUTIES AND RESPONSIBILITIES: Our employees are tasked with delivering excellent business results through the efforts of their teams.  These results are achieved by: Enablement & SDLC Transformation 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. Agent Architecture & Deployment 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. Governance & Compliance 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. Delivery Leadership 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. Stakeholder Interface 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. Consistent with the Americans with Disabilities Act (ADA) and applicable state and local laws, it is the policy of EVERSANA to provide reasonable accommodation when requested by an employee with a disability, unless such accommodation would cause an undue hardship for EVERSANA. If reasonable accommodation is needed to perform the essential functions of your job position, please contact Human Resources. EXPECTATIONS OF THE JOB: Travel (Minimal) Hours (40 hours, Monday through Friday) The above list reflects the general details necessary to describe the expectations of the position and shall not be construed as the only expectations that may be assigned for the position. An individual in this position must be able to successfully perform the expectations listed above MINIMUM KNOWLEDGE, SKILLS AND ABILITIES: 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. PREFERRED QUALIFICATIONS: 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. PHYSICAL/MENTAL DEMANDS AND WORKING ENVIRONMENT: The physical and mental requirements along with the work environment characteristics described here are representative of those an individual encounters while performing the essential functions of this