Engineer II, AI

• Contribute towards end-to-end ML development lifecycle, including data preparation, model building, deployment, and monitoring for revenue cycle applications.
• Build and optimize Gradient Boosting Trees models using LightGBM, and develop scikit-learn pipelines for predictive analytics.
• Implement Large Language Model (LLM) operations with OpenAI APIs, including summarization, prompt engineering, fine-tuning, and integration into operational workflows.
• Apply Explainable AI (XAI) techniques using libraries like SHAP and LIME to interpret model decisions and ensure transparency in high-stakes healthcare scenarios.
• Analyze complex datasets to identify patterns, develop deployable solutions, and collaborate on production-grade ML systems.

Required Skills

• Proficiency in Python and SQL for high-quality, production-ready code, with a public GitHub repository showcasing ML projects.
• MUST have actively programmed in python for 3 years
• SHOULD know SQL
• Expertise in ML libraries: scikit-learn, PySpark ML.
• Expertise in data manipulation libraries: pandas, Dask, Polars, PySpark.
• Expertise in data validation tools: Pydantic, Pandera.
• The right candidate should be able to pick up new technologies rapidly and contribute towards key initiatives.
• Hands-on experience with XAI libraries including but not limited to LIME, SHAP, BLEU, ROUGE etc.
• Strong knowledge of LLMs (OpenAI), including prompt engineering, tuning, and summarization tasks.
• Experience in healthcare or revenue cycle management is a plus.

Preferable Skills

• Exposure to healthcare analytics or revenue cycle management is advantageous but not mandatory. Such experience enhances the ability to apply ML solutions directly to domain-specific challenges in Ensemble’s operations. Candidates with this background can accelerate impact in revenue cycle optimization.
• Knowledge of Spark and experience in Databricks is a plus

Qualifications

• Undergraduate degree (B.E./B.Tech) in Engineering or Technology, or a graduate degree (M.Sc./M.S.) in Science, Mathematics, or Statistics (STEM).
• A first class, distinction, or top 10 percentile performance throughout the academic career is mandatory.
  • Relevant experience exceeding 3 years is a key requirement alongside these academic credentials. People with a data/analytics background before the 3 years in ML Engineering with be preferred.