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EVERSANA is establishing an AI Hub Center of Excellence within Patient Services Technology to transform software development practices. The AI Engineering & Enablement Lead will own this strategic mission: ingesting enterprise AI tooling into the Patient Services SDLC, building a governed framework for deploying and maintaining AI agents, establishing engineering best practices, and re-architecting the engineering organization into an AI-augmented software development model.
This is a player-coach role requiring both hands-on technical leadership and team management. The Lead serves as 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 activities. You will be accountable for converting the AI Hub roadmap from backlog items into shipped, governed, production-grade software delivered by an AI-accelerated team.
Key responsibilities include: owning the AI Hub COE charter and bridging EVERSANA's Enterprise AI team with Patient Services engineering; ingesting and operationalizing enterprise AI tooling (GCP Vertex AI, Claude, Gemini Enterprise) into daily SDLC workflows for ACTICS (Salesforce Health Cloud), MuleSoft, and Java/.NET teams; re-architecting engineering practice to standardize AI-assisted development using Claude Code, Cursor, and GitHub Copilot across Dev, QA, and BA functions; architecting agent deployment and lifecycle frameworks on Vertex AI with reusable patterns for RAG pipelines, tool/function calling, and multi-step orchestration; owning AI governance for Patient Services including model selection, PHI/HIPAA handling, and compliance frameworks; leading a hybrid onshore/offshore follow-the-sun delivery model; defining AI velocity KPIs and reporting quarterly ROI to CTO and CFO; and serving as the senior technical voice for AI initiatives with executive leadership and vendor partners.
Required qualifications: 8+ years in software engineering with 3+ years in technical lead or architect capacity; demonstrated production experience architecting and deploying LLM-based systems or AI agents (not prototypes); hands-on fluency with a major cloud AI platform (Vertex AI preferred; AWS Bedrock or Azure OpenAI acceptable); working knowledge of agent design patterns, RAG, function calling, and MCP servers; healthcare or regulated-industry software experience strongly preferred; and proven ability to lead technical transformation and change management.
