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Clinical Prompt Engineer

Truveta - Hyderabad, India

Posted May 8, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Market context

Median wage (BLS OEWS)
$111,944 national median
Projected growth (BLS Employment Projections)
+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work
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Application

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Where they hire

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About this role

Clinical Prompt Engineer Hyderabad, India Truveta provides unprecedented real-world data and real-time intelligence, powered by a dataset built with and owned by US health systems united in a mission of Saving Lives with Data. Together, we power breakthrough medical discoveries, accelerate regulatory-grade evidence, and improve patient care. Today, Truveta enables research on more than 130 million de-identified patients across the US. Achieving Truveta's ambitious mission requires an incredible team of talented and inspired people with a special combination of health, software and big data experience who share our company values . Role Overview We are hiring a Prompt Engineer with a strong clinical informatics background to build and optimize AI prompts for healthcare and clinical data applications. This role requires hands-on clinical understanding, experience working with real clinical data, experience writing prompts for AI models, and the ability to align LLM prompts with underlying clinical data models. You will work closely with clinicians, data engineers, and AI teams to translate clinical questions and workflows into accurate, safe, and scalable AI interactions. Key Responsibilities - Design, test, and optimize prompts for LLM-based clinical use cases such as: - Cohort identification - Clinical question answering - Concept extraction - Clinical code mapping - Translate clinical requirements into structured prompts aligned with clinical data schemas - Collaborate with data engineers to understand how diagnoses, labs, medications, encounters, and outcomes are modeled - Evaluate AI outputs for clinical accuracy, relevance, bias, and safety - Apply healthcare data standards (FHIR, SNOMED CT, LOINC, RxNorm, ICD-10-CM)

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