• Hire, develop, and lead a high-caliber team of AI Forward Deployed Engineers with healthcare domain depth. • Define the AI FDE operating model, engagement patterns, and delivery standards for healthcare customers. • Create a culture of speed, accountability, technical excellence, and patient-impact awareness. • Establish clear career paths, skill expectations, and performance metrics for FDEs. • Own end-to-end success of AI deployments at healthcare customers—from discovery to production. • Partner directly with CIOs, CMIOs, CTOs, clinical leaders, data leaders, and operations executives. • Act as the executive escalation point for complex AI delivery, data, and adoption challenges. • Ensure AI solutions are clinically safe, operationally usable, and embedded in real healthcare workflows. • Provide hands-on technical leadership across agentic AI architectures applied to healthcare use cases. • Drive adoption of modern frameworks and patterns, including: LangGraph, CrewAI, AutoGen, Semantic Kernel, LlamaIndex Agents, Haystack, Workflow orchestration platforms such as n8n, Temporal, Prefect, Dagster, MCP (Model Context Protocol), function calling, and tool-using agents. • Ensure AI agents are designed with strong guardrails and failure handling, human-in-the-loop workflows, explainability and auditability, healthcare-grade observability and logging. • Translate customer-specific healthcare AI solutions into reusable Gravity platform capabilities. • Influence the Gravity AI roadmap based on real-world healthcare deployments. • Partner with Product, Engineering, Security, Compliance, and Privacy teams to ensure HIPAA-aligned AI deployments. • Ensure learnings from healthcare deployments are captured and upstreamed. Outcomes, Adoption & Scale. • Own success metrics for AI FDE engagements, including: Time-to-value for healthcare customers, Adoption and usage by clinical and operational users, Stability, reliability, and data quality, Customer satisfaction (NPS/CSAT). • Drive repeatable AI deployment playbooks that scale across healthcare customers. • Ensure AI delivery compounds into long-term platform value, not one-off implementations.

• Own the support queue end-to-end: triage, investigate, resolve, and follow up with customers via Support Desk and email • Create clear bug reports (steps to reproduce, screenshots, expected vs actual behavior) and track escalations to Engineering • Improve support workflows and infrastructure: macros, tagging, routing, SLAs/expectations, and internal playbooks • Evaluate and recommend support tooling and processes to support scale (e.g., Zendesk vs Intercom) • Maintain and improve customer-facing and internal support documentation with manager (Help Docs, quick guides, Looms) • Participate in product QA/testing (including weekly bug bash) and proactively test workflows ahead of customer sessions • Monitor recurring issues and customer friction patterns; share insights with Product/Engineering to prevent repeats • Collaborate cross-functionally to support launches, incidents, and customer communications when needed

• You will be the "Manager" of our digital sales team. • You will be supported by Sales Enablement (for messaging), Operations (for data), and Sales Leadership to ensure your agents are performing at the highest level. • Design and configure autonomous agents using Salesforce Agentforce, Flow, and Data Cloud. • Create agents capable of handling the entire B2C sales cycle, from initial outreach and objection handling to final transactions and closing the deal without human intervention. • Architect agents that engage B2B leads, identify upsell opportunities, qualify budget/authority, and seamlessly hand off "hot" leads to human sales reps. • Write the "brain" of the agent, translating sales methodologies into system prompts and logic gates. • Monitor their conversation logs, analyze win/loss rates, A/B test different outreach personas, and continuously refine their ability to convert. • Ensure agents have access to the right data (CRM history, product inventory, pricing) to make accurate, hallucination-free decisions.

• Develop AI Agent Systems: Build intelligent search and task execution agents using ReAct, planning, and multi-agent frameworks (e.g., LangGraph, Dify, CrewAI) • Optimize End-to-End RAG Pipelines: Build and refine efficient RAG systems from ingestion, chunking, and embedding to hybrid vector search (OpenSearch), implementing precise grounding and citation • LLM Training & Alignment: Conduct advanced post-training (SFT, RLHF, continual pretraining) and align models for reliable JSON-schema function calling and external tool usage • Automated Evaluation & Iteration: Build offline/online evaluation pipelines using synthetic QA, retrieval metrics, and hallucination detection to continuously improve system performance and stability