Advisor – Software Engineering, Evidence Intelligence

Eli Lilly and Company

. Build and maintain ingestion pipelines that parse Lilly clinical evidence into structured formats for large language model retrieval .

Posted 9/15/2026full-timeRemote • United StatesMid-LevelSenior💰 $144,000 - $231,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining cloud-based data infrastructure, including ingestion pipelines and API design, while ensuring data integrity and compliance in regulated environments. Proficient in programming languages such as Python and SQL, with hands-on experience in integrating AI platforms and managing clinical data.

Highest-signal resume keywords

Cloud-Based Data InfrastructureAPI Design and DevelopmentData Pipeline ManagementModel Context Protocol ImplementationLife Sciences Regulatory Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLData Pipeline DevelopmentAPI DevelopmentJavaScriptTypeScriptModel Context ProtocolRetrieval-Augmented Generation (RAG)Cloud InfrastructureData Transformation

Soft Skills

CollaborationProblem-Solving

Tools & Technologies

AI PlatformsIntegration ToolsServer Configurations

Industry Keywords

Clinical DataRegulatory SubmissionsTrial DatasetsLife Sciences

Tech Stack

Tools & technologies

CloudJavaScriptPythonSQLTypeScript

About the role

Key responsibilities & impact

Build and maintain ingestion pipelines that parse Lilly clinical evidence into structured formats for large language model retrieval
Design, build and maintain the API and storage layer that stores Lilly clinical evidence and serves it through pre-defined structured requests
Configure and maintain server and tool-call integrations connecting the infrastructure to partner AI platforms
Implement authentication, per-partner access controls, and audit logging across delivery surfaces
Monitor pipeline and platform health
Resolve data integrity, latency, or availability issues affecting the evidence feed to AI platforms
Collaborate with scientists, researchers, and business stakeholders

Requirements

What you’ll need

Bachelor's degree in computer science, software engineering, or a related technical field
5 years of professional software engineering experience
3 years building and operating cloud-based data infrastructure
Experience building tool-calling or connector integrations between backend infrastructure and LLM platforms, such as Model Context Protocol servers, function calling, or plugin frameworks
Proficiency in programming languages such Python and SQL
Hands-on experience building and maintaining data pipelines, including ingestion, transformation, and structured output
Experience designing and building APIs (REST or equivalent) that serve structured data at scale
Hands-on experience implementing a Model Context Protocol server
Experience implementing retrieval-augmented generation (RAG) pipelines or working within LLM-adjacent technical frameworks
Experience with JavaScript or TypeScript and modern web frameworks
Experience working in life sciences or another regulated industry, including hands-on work with healthcare or clinical data structures, regulatory submissions, or trial datasets

Benefits

Comp & perks

Company bonus depending, in part, on company and individual performance
Company-sponsored 401(k)
Pension
Vacation benefits
Medical, dental, vision and prescription drug benefits
Flexible benefits, including healthcare and/or dependent day care flexible spending accounts
Life insurance and death benefits
Time off and leave of absence benefits
Well-being benefits, including employee assistance program, fitness benefits, and employee clubs and activities