Applied Engineer, Agentic AI in Center of Excellence

Job Summary

Raapid is a leading healthcare technology innovator specializing in AI-enabled risk adjustment solutions. Funded by Microsoft and Great Place to Work-certified organization, we serve payers, healthcare providers, and support organizations with cutting-edge technology solutions that optimize revenue, ensure compliance, and reduce administrative costs.

Position Overview

We are establishing an Agentic AI Center of Excellence (CoE) to design, build, and deploy the next generation of autonomous digital workers across Raapid. We are seeking pragmatic, high-velocity Applied AI Engineers, Senior Applied AI Engineers, and Principal Applied AI Engineers to join this specialized team as core Builders. This is a pure Individual Contributor (IC) track designed for engineers who love being hands-on with code and shipping production AI systems.

As a Builder, you will not just write code in a sandbox - you will own the entire lifecycle of your creations. You will be responsible for building AI agents, deploying them to production, maintaining their operational health, and continuously improving their accuracy, cost-efficiency, and user experience based on real-world usage and structured evaluation. You will work closely with CoE Champions (functional sponsors across Sales, Marketing, Product, Operations, HR, Legal, and Finance) and Translators (understanding of product + building) to convert business workflows into production-grade agentic systems that move the needle on revenue, efficiency, and quality of care.

Responsibilities

• End-to-End Lifecycle Ownership (Build, Deploy, Maintain & Improve)
  • Build: Design and code autonomous AI agents, custom API tools, and Retrieval-Augmented Generation (RAG) pipelines using modern orchestration frameworks (LangGraph, CrewAI, AutoGen, etc).
  • Deploy: Set up robust deployment pipelines to move AI agents from local MVPs to production-grade environments using Docker, APIs, microservices, and cloud infrastructure.
  • Maintain: Take full responsibility for the operational health of your deployed agents - monitor uptime, handle error states, manage token/cost limits, and ensure day-to-day reliability.
  • Improve: Establish systematic evaluation loops (Evals) to measure agent accuracy. Continuously refine prompt structures, system guidelines, guardrails, and memory retrieval mechanisms to improve agent performance over time.
• Technical Implementation & Data Foundation
  • Agentic Frameworks: Build and manage autonomous AI agents for tasks such as lead prospecting, market segmentation, content generation, claims/coding workflows, operations management, and data synthesis.
  • Data Integration: Implement and optimize indexing, hybrid search, and RAG pipelines using vector databases (Pinecone, Qdrant, Milvus, Chroma) or knowledge graphs (Neo4j) to supply high-context data to agents - critical for Raapid s grounded use cases.
  • API Integration & Tooling: Connect LLMs to internal enterprise systems (CRM, EHR/FHIR endpoints, databases, operational tools) and external APIs, enabling agents to execute real-world actions safely.
• AI Reasoning, Evaluations & Guardrails
  • Reasoning & Evaluation: Define, test, and run automated evaluation frameworks (Ragas, Lang Smith, or custom eval datasets) to track latency, accuracy, safety, and hallucination rates across agent workflows.
  • Guardrails & Security: Implement strict guardrails to ensure agents operate safely, protect PHI/PII, and adhere to HIPAA, HITRUST, and other compliance standards intrinsic to healthcare AI.
  • Cost & Latency Optimization: Tune model selection, caching, batching, and prompt design to keep token economics and end-to-end latency within production SLOs.
• Cross-Functional Collaboration
  • Collaborate with Translators: Partner with CoE Translators to convert business workflows into production-ready agent architectures, treating their domain context as a first-class design input.
  • Support Business Units: Act as a technical advisor to Sales, Marketing, Product, Operations, HR, Legal, and Finance Champions on feasibility, ROI, and best practices for automating their workflows through Agentic AI.
  • Knowledge Sharing: Document architectural decisions, share reusable agent patterns, and contribute to the CoE s internal library of components, prompts, evals, and guardrails.

Qualifications

• Technical Degree: Bachelor s or Master s degree in Computer Science, Software Engineering, Information Technology, or a related technical field.
• Provable Portfolio: A strong, active GitHub profile featuring at least 3 to 5 repositories demonstrating hands-on experience building, deploying, and maintaining software applications - ideally including at least one project that integrates LLMs or AI APIs and solves a real business problem.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.