ML Engineer

About this role

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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 unde...