AWS Data Scientist

Job Description Job Title : - Data Science/MLOps Engineer Location : - Houston, TX (3 days onsite/Week) Type of Employment : - Contract Duration : - Long Term Note : - The final interview will be in person. If the candidate is local, they will need to attend in Houston, TX. If the candidate is non-local, they will be required to visit a nearby location for the in-person interview. What we are looking for ! Build, deploy, and operationalize scalable AI-powered clinical NLP and machine learning solutions using deep learning, LLMs, and cloud-native big data platforms in healthcare environments. we are looking for candidates who have strong hands-on experience in: • MLOps practices (model deployment, monitoring, CI/CD, automation, retraining) • Cloud-based ML platforms and production environments • Data Science and applied machine learning Mandatory skills • Strong hands-on expertise in Natural Language Processing (NLP), machine learning, and deep learning • Proficiency in Python for building and deploying NLP and ML solutions • Experience working with Large Language Models (LLMs), prompt engineering, and agentic workflows (e.g., Lang Graph or similar frameworks) • Solid understanding of data pre-processing, normalization, feature extraction, and quality validation techniques • Strong MLOps experience, including model versioning, pipeline orchestration, CI/CD, monitoring, performance tracking, and retraining strategies • Experience to containerization and orchestration tools (Docker, Kubernetes) • Proficiency in SQL for data querying, transformation, and analytics • Practical experience with AWS big data and compute services (EMR, Spark, PySpark) • Working knowledge of AWS services, including AWS Bedrock for generative AI use cases • Experience with at least one relational database: PostgreSQL or MySQL · Strong understanding of testing strategies, error analysis, and model validation techniques Good-to-Have Skills • Prior experience with clinical, biomedical, or healthcare NLP use cases • Familiarity with healthcare data standards, terminologies, or ontologies • Experience deploying ML/NLP solutions in regulated or production healthcare environments • Knowledge of distributed systems and cloud-native data architectures • Experience with additional data stores, data warehouses, or NoSQL technologies • Strong technical documentation and stakeholder communication skills • Experience working in agile or cross-functional product development teams