AI Engineer, Agent/Platform Tracks

Parexel

AI Engineer developing specialized pharmacovigilance agents to enhance clinical research safety at Parexel. Collaborating with cross-functional teams to implement AI solutions using large language models.

Posted 7/27/2026full-timeRemote • 🇮🇳 IndiaMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AWSPython

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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