AI Engineering & Enablement Lead

Eversana1; United States; Hybrid; USD 180,000 - 240,000; Full time

Job summary

EVERSANA is establishing an AI Hub Center of Excellence within Patient Services Technology. The AI Engineering & Enablement Lead will own the mission of integrating enterprise AI tooling into the SDLC, establishing governance, and re-architecting engineering practices into an AI-augmented software development organization.

This is a player-coach role leading a hybrid onshore/offshore team, aligning stakeholders, and delivering production-grade AI-enabled software from a backlogged roadmap.

Qualifications

• 8+ years in software engineering with leadership experience.
• Demonstrated AI/LLM deployment in production environments.
• Fluency with major cloud AI platforms and leading LLMs.
• Experience introducing AI tooling into engineering organizations.
• Knowledge of Salesforce ecosystem and enterprise integrations.

Responsibilities

• Own the AI Hub COE charter and bridge between AI teams and Patient Services engineering.
• Ingest and operationalize enterprise AI tooling into SDLC for ACTICS, MuleSoft, and Java/.NET teams.
• Architect AI-driven development, standardizing AI-assisted tooling across teams.
• Define change-management paths to adopt AI-first workflows.

Skills

Technical leadership

Architecting AI systems

Cloud platforms

AI tooling adoption

Stakeholder communication

Tools

Vertex AI

Claude

Gemini Enterprise

MuleSoft

GitHub Copilot

Cursor

LangChain

CrewAI

Vertex Agent Builder

Job description

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:

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