Machine Learning Engineer

Position: Machine Learning Engineer Location: Remote Duration: Contract 6-12 months contract might extend Key Responsibilities • Design, develop, and deploy machine learning models for classification, prediction, NLP, and recommendation use cases • Build end-to-end ML pipelines including data ingestion, feature engineering, model training, evaluation, and deployment • Work with large-scale healthcare datasets (claims, clinical, member, provider, and operational data) • Develop and optimize GenAI/LLM-based solutions (prompt engineering, RAG pipelines, embeddings, fine-tuning where applicable) • Collaborate with data engineers to ensure reliable, scalable, and secure data pipelines • Deploy models using cloud-native services (Azure preferred for UHG) • Implement MLOps practices including CI/CD, model versioning, monitoring, drift detection, and retraining • Ensure compliance with healthcare data security and privacy standards (HIPAA, PHI handling) • Partner with product owners, architects, and business stakeholders to translate business requirements into ML solutions • Document solutions, models, and design decisions for auditability and knowledge transfer Required Skills Machine Learning & AI Strong hands-on experience with supervised and unsupervised ML algorithms Experience in NLP, text analytics, and information extraction Practical exposure to GenAI/LLM frameworks (RAG, embeddings, vector databases) Programming Proficiency in Python Experience with ML libraries: scikit-learn, TensorFlow Strong SQL skills for data analysis Cloud & MLOps Experience with Azure ML, Azure Databricks, Azure Data Factory (or equivalent cloud ML stack) Familiarity with Docker, CI/CD pipelines, and model monitoring Understanding of model lifecycle management Data Strong understanding of data preprocessing, feature engineering, and data quality Experience working with structured and unstructured data