AI/ML Engineer - Healthcare IT

Milliman is seeking a passionate and skilled Machine Learning and AI Engineer to join our innovative Healthcare IT team in Gurgaon/Gurugram, India. This role is central to developing cutting-edge AI models, including Generative AI and agentic AI systems, to analyze complex healthcare claims datasets. You will extract valuable insights and build advanced analytics such as models for anomaly detection and predictive insights. The ideal candidate possesses strong experience in machine learning, data analysis, and cloud platforms, particularly Databricks and Azure, and will significantly shape the future of AI-driven healthcare solutions.

Key Responsibilities

AI & Machine Learning Model Development

• Design, develop, and optimize AI/ML models for tasks including anomaly detection, fraud detection, and predictive analytics using healthcare claims data.
• Implement and fine-tune Generative AI and agentic AI algorithms for data synthesis and decision-making.

Data Engineering & Preprocessing

• Process, clean, and transform large-scale structured and unstructured healthcare claims data using Pyspark and other tools for ML pipelines.
• Conduct exploratory data analysis to understand data characteristics and identify potential insights.
• Engineer relevant features to enhance model performance.
• Develop and fine-tune machine learning models using techniques such as regression, classification, clustering, and time series analysis.
• Apply advanced techniques like deep learning and natural language processing where appropriate.
• Build and maintain scalable data pipelines on Databricks and Azure.

Collaboration & Implementation

• Collaborate with cross-functional teams, including data engineers, software developers, and healthcare domain experts, to integrate AI solutions into existing workflows.
• Deploy machine learning models into production environments and monitor their performance.

Research & Innovation

• Stay abreast of advancements in AI/ML technologies and propose innovative solutions for complex healthcare challenges.
• Experiment with state-of-the-art frameworks and techniques to improve model performance and scalability.

Documentation & Compliance

• Communicate complex technical concepts effectively to both technical and non-technical audiences.
• Ensure all models and workflows adhere to relevant data privacy and security standards, such as HIPAA.
• Document processes, results, and best practices for knowledge sharing and reproducibility.

Qualifications

Educational Background

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, or a related field.
• Certifications in Azure, Databricks, or relevant AI/ML technologies are advantageous.

Professional Experience

• 2-5 years of hands-on experience in designing and implementing machine learning models, preferably within the healthcare or insurance sectors.
• Proven experience working with cloud-based platforms like Azure and Databricks for data processing and model deployment.

Technical Expertise

• Strong proficiency in Python, SQL, and relevant ML libraries/frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Expertise in data manipulation and analysis using Pandas, NumPy, and PySpark, with a focus on Azure Databricks.
• Experience in building and fine-tuning anomaly detection algorithms and predictive models.
• Proficiency with data visualization tools including Power BI, Tableau, Matplotlib, and Seaborn.

Domain Knowledge

• Familiarity with healthcare claims data structures, terminologies (e.g., ICD codes, CPT codes), and workflows.
• Understanding of healthcare compliance and data privacy standards like HIPAA.

Soft Skills

• Exceptional analytical and problem-solving abilities with keen attention to detail.
• Excellent communication and collaboration skills for effective teamwork.
• Capacity to manage multiple priorities in a dynamic environment.
• Ability to work both independently and collaboratively.
• A genuine passion for data and a drive to uncover insights.

Preferred Qualifications

• Experience with Generative AI frameworks (e.g., GPT models, VAEs) and agentic AI.
• Knowledge of healthcare fraud detection systems or predictive analytics in the insurance industry.
• Familiarity with MLOps practices, including model versioning, monitoring, and CI/CD pipelines.