ML Engineer

Job Position - ML Engineer

Core skill -      Build and deploy ML Models. Azure Cloud is preferred.

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, 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, 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