A seasoned machine learning and AI application engineer with strong knowledge and hands-on experience on
Summary
A seasoned machine learning and AI application engineer with strong knowledge and hands-on experience on
genomic/clinical data modeling and machine learning , AI/GenAI application development, data engineering, and cloud computing.
Qualifications
Education:
Master's or higher degree in Computer Engineering, Data Science, Bioinformatics, Machine Learning and Data modeling, or related field with five (5) years of experience in genomic data analysis and application development.
Experience:
Hands-on experience in clinical bioinformatics pipeline development including secondary/tertiary analysis, variant interpretation and classification pipeline R&D, and automated report generation
Hands-on experience in human genetics/multi-omics data modeling and application development especially in next-generation sequencing data
Hands-on experience in machine learning framework (such as Huggingface, TensorFlow)
Hands-on experience in automated and scalable AI/GenAI application evaluation, development, and deployment.
Hands-on experience in RAG AI framework
Hands-on experience with scripting language, such as Bash and Python
Strong experience in cloud platform (Azure) and data services (data lakehouse/data warehouse)
Experience in context-aware OCR
Experience in databases, including SQL and no-SQL
Familiarity with advanced data visualization techniques
DevOps experience such as unit testing, CI/CD is a plus.
Strong curiosity and the ability to learn quickly and adapt to a fast-changing environment
Duties And Responsibilities
Serves as the SME in Bioinformatics ML/AI application development in a clinical genetic testing setting. Provides hands-on support towards building company’s next-generation bioinformatics ML/AI platform
Designs, develops, evaluates, and deploys state-of-the-art ML/AI solutions to gain valuable data insights based on the genetical, phenotypical, and clinical datasets
Evaluates, adopts, and customizes GenAI models based on both internal and external datasets to enhance the overall performance of the genetic testing workflow
Supports both internal and external data requirements by leveraging AI/ML and GenAI capabilities to keep up with the increasing demands of the business
Collaborates in a multidisciplinary and regulated clinical diagnostics environment with geneticists, bioinformaticians, software engineers, and IT infrastructure professionals
Physical Demands And Work Environment
Frequently required to sit
Frequently required to stand
Frequently required to utilize hand and finger dexterity
Frequently required to talk or hear
Frequently required to utilize visual acuity to operate equipment, read technical information, and/or use a keyboard
Occasionally exposed to bloodborne and airborne pathogens or infectious materials
EEO Statement
Baylor Genetics is proud to be an equal opportunity employer dedicated to building an inclusive and diverse workforce. We do not discriminate based on race, religion, color, national origin, sex, sexual orientation, age, gender identity, veteran status, disability, genetic information, pregnancy, childbirth, or related medical conditions, or any other status protected under applicable federal, state, or local law.
Seniority level
Seniority level Mid-Senior level
Employment type
Employment type Full-time
Job function
Job function Engineering and Information Technology
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