Machine Learning Senior Analyst

You will be part of a team focused on transforming healthcare and improving the lives of millions of members through innovative AI/ML solutions.

The role involves developing and supporting machine learning models and data-driven solutions for use cases such as document processing, analytics, and intelligent automation.

As a Machine Learning Senior Analyst, you will work closely with senior engineers, product teams, and business stakeholders to build, test, and deploy scalable AI solutions.

Responsibilities

• Develop and implement machine learning models under guidance to solve business problems in healthcare.
• Work on data preprocessing, feature engineering, and model evaluation as part of AI/ML pipelines.
• Assist in building and integrating ML solutions with APIs and applications.
• Collaborate with cross-functional teams (engineering, product, data teams) to understand requirements and deliver solutions.
• Support development of AI solutions that adhere to privacy, security, and responsible AI principles.
• Work with structured and unstructured data, including text data for NLP use cases.
• Assist in optimizing model performance and improving accuracy through iterative testing.
• Create basic dashboards, reports, or visualizations to present insights.
• Participate in code reviews, testing, and debugging to ensure quality delivery.
• Document solutions, workflows, and model behavior for reuse and knowledge sharing.

Generative AI & LLM Responsibilities

• Design and optimize prompts for Large Language Models (LLMs) to achieve desired outputs for various use cases.
• Build and maintain Retrieval-Augmented Generation (RAG) pipelines for document Q&A, knowledge retrieval, and contextual response generation.
• Develop and support agentic AI workflows using frameworks such as LangChain, LangGraph, or similar orchestration tools.
• Implement and manage ETL pipelines for data ingestion, transformation, and preparation for ML/GenAI workloads.
• Deploy and manage AI/ML solutions on AWS cloud infrastructure (SageMaker, Lambda, Bedrock, S3, etc.).
• Monitor and evaluate LLM performance, including response quality, latency, and cost optimization.

Requirements

• 4–5 years of experience in Machine Learning / Data Science / AI domain.
• Develop and support agentic AI workflows using frameworks such as LangChain, LangGraph, or similar orchestration tools.
• Hands-on experience with Python and libraries such as Pandas, NumPy, Scikit-learn.
• Basic understanding of machine learning concepts such as classification, regression, clustering, and NLP.
• Exposure to model development lifecycle (data preparation, training, evaluation).
• Experience working in a team environment on shared deliverables or projects.

Desired Experience

• Exposure to Generative AI / LLM concepts and prompt engineering.
• Familiarity with frameworks such as PyTorch or TensorFlow.
• Experience with APIs, data integration, or cloud platforms (Azure/AWS/GCP).
• Knowledge of vector databases or basic information retrieval concepts.
• Experience working with data formats like CSV, JSON, or documents (PDF, Word).

Education and Training Required

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or related field.