Overview

Job Responsibilities

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

Required Skills

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

Preferable Skills

1. 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.
2. Knowledge of Spark and experience in Databricks is a plus

Qualifications

1. Undergraduate degree (B.E./B.Tech) in Engineering or Technology, or a graduate degree (M.Sc./M.S.) in Science, Mathematics, or Statistics (STEM).
2. A first class, distinction, or top 10 percentile performance throughout the academic career is mandatory.
3. 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.