AI Engineer, Agent/Platform Tracks

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Parexel

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1983

💼 Consulting

🏥 Healthcare

📦 Logistics

💰 Venture Round on 1990-01

Consulting • Healthcare • Logistics

Parexel is one of the world's largest clinical research organizations (CROs), providing comprehensive services for the clinical development process from Phase I to IV. The company specializes in portfolio management, clinical trial management, regulatory strategy, market access, and lifecycle management for biopharmaceuticals. Parexel aims to speed life-changing medicines to market by leveraging its clinical, regulatory, and therapeutic expertise. With a global team of over 21,000 professionals, Parexel works to integrate patient insights and innovative trial designs to develop treatments across therapeutic areas such as oncology, neuroscience, rare diseases, and more. It focuses on delivering patient-centric and efficient clinical trials.

📋 Description

• 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

🎯 Requirements

• 3+ years of software engineering experience, with at least 1 year building applications that use LLM APIs (Anthropic, OpenAI, or equivalent) • Proficiency in Python with demonstrated experience in production environments • Experience building and evaluating NLP or LLM-based systems with measurable quality metrics • Strong problem-solving skills and ability to work independently while collaborating with team members • Bachelor's degree in computer science, or a related field, or equivalent professional experience • Strong prompt engineering skills and experience writing and iterating system prompts, few-shot examples, chain-of-thought patterns, and structured output formats • Solid foundation in Python with hands-on experience in LLM orchestration frameworks such as LangChain, LangGraph, or similar tools • Experience building evaluation pipelines for NLP or LLM outputs: precision/recall measurement, confusion matrices, and threshold tuning • Comfort with AWS services including S3, Lambda, and IAM basics, with AWS Bedrock experience being a plus • Excellent written communication skills: ability to document prompt design decisions, evaluation results, and agent behavior specifications for validation purposes • A collaborative mindset and ability to work effectively with cross-functional teams including domain experts and platform engineers

🏖️ Benefits

• Flexibility, growth, and creating space for people to do their best work