AI Engineer

Implement the specialized pharmacovigilance agents: write system prompts, configure model parameters, build tool-use definitions, and define agent boundaries to ensure precise adverse event processing

Build and iterate prompt chains for each processing step: source document parsing, field extraction, MedDRA coding suggestions, causality assessment logic, narrative drafting, and E2B(R3) output generation

Develop the deterministic rule engine layer: implement ICH E2B field validation checks, MedDRA hierarchy verification, and regulatory logic constraints that operate alongside LLM outputs

Create and maintain evaluation datasets in collaboration with the pharmacovigilance domain team: annotated ground-truth cases, edge case libraries, and regression test suites

Develop and maintain Model Context Protocol (MCP) servers to expose enterprise applications, APIs, databases, and services as standardized tools for AI agents

Implement secure MCP integrations, tool definitions, authentication, and testing to enable reliable agent interaction with internal and external systems

Run accuracy benchmarks, analyze failure modes, and iterate on prompts and agent configurations to improve performance against defined thresholds

Implement the quality control agent's cross-verification logic: configure separate Claude instances, build comparison algorithms, and calibrate confidence scoring

• Build human-in-the-loop feedback mechanisms: reviewer interfaces for accept/modify/reject decisions, structured feedback capture, and feedback-to-prompt-improvement pipelines