GenAI Developer

Summary

Join our clients innovative team as an experienced GenAI Developer with a minimum of 5 years of hands-on experience in the intersection of AI and genetics. If you are passionate about leveraging AI to advance genetic research, we want to hear from you.

Requirements

• A minimum of 5 years of professional experience as a GenAI Developer with a focus on applying AI in genetics
• Proficiency in machine learning, deep learning, and data mining techniques
• Strong programming skills in languages such as Python or R
• Experience with genetic data formats (e.g., VCF, PLINK) and genomic databases
• Knowledge of relevant libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
• Strong understanding of genetics and genomics, including GWAS and NGS data
• Experience working with cloud computing platforms for large-scale data processing
• Excellent problem-solving skills and a data-driven mindset
• Strong communication and collaboration skills
• Ability to work independently and in a team
• Research publications or contributions to the field are a plus

Responsibilities

• Develop and implement advanced AI and machine learning algorithms for genetic data analysis and interpretation
• Collaborate with geneticists, biologists, and data scientists to define and address research questions and hypotheses
• Design and develop computational pipelines for processing and analyzing large-scale genetic datasets
• Implement data preprocessing, feature engineering, and quality control procedures
• Develop predictive models and algorithms for genotype-phenotype associations and genetic risk prediction
• Optimize AI models for performance and scalability to handle vast genetic datasets
• Conduct statistical analyses to interpret genetic variation and disease associations
• Collaborate on the development of software tools and applications that support genetic research and diagnostics
• Stay current with the latest advancements in AI, machine learning, and genetic research
• Mentor junior team members and share knowledge within the research team