AI / ML Engineer

Overview

Develop AI- and machine-learning-driven applications and systems, including production-ready cloud or on-premises pipelines. This role focuses on traditional data science, including regression, classification, statistical modeling, predictive analytics, and model deployment.

What you'll do

• Develop applications and systems that utilize AI tools and Cloud AI services with production-ready cloud or on-premises application pipelines.
• Apply GenAI models as part of solutions, along with deep learning, neural networks, chatbots, and image processing where applicable.
• Act as an SME, collaborate with and manage the team, and make team decisions.
• Engage with multiple teams and contribute to key decisions.
• Provide solutions to problems for the immediate team and across multiple teams.
• Lead the design and implementation of AI-driven solutions aligned with business objectives.
• Facilitate knowledge sharing and mentoring within the team.
• Coordinate with stakeholders to gather requirements and translate them into technical specifications.
• Monitor project progress and implement improvements to optimize workflows and deliverables.
• Develop, train, and deploy machine-learning models for classification, regression, and predictive analytics use cases.
• Perform data exploration, preprocessing, and feature engineering on large and complex datasets.
• Apply statistical techniques, including hypothesis testing, distributions, and variance analysis, to derive insights.
• Design and execute model validation frameworks, including cross-validation, A/B testing, and performance tuning.
• Conduct hyperparameter tuning to improve model accuracy and efficiency.
• Write optimized and scalable SQL queries for data extraction, transformation, and analysis.
• Build reusable and scalable Python-based data-science pipelines.
• Collaborate with data engineers and business stakeholders to translate requirements into analytical solutions.
• Ensure model performance monitoring, documentation, and reproducibility.
• Communicate findings, insights, and model outcomes to technical and non-technical stakeholders.

What you'll need

• Minimum 5 year(s) of experience is required.
• Minimum 8 years of experience in Data Science.
• 15 years full time education.
• Data Science, Machine Learning (ML), Python Frameworks, and MySQL.
• Python, including Pandas, NumPy, and Scikit-learn.
• SQL, including advanced querying, optimization, and transformations.
• Solid understanding of statistics and probability, including distributions, hypothesis testing, variance, and confidence intervals.
• Hands-on experience with Logistic Regression, Linear Regression, and tree-based models including Random Forest, Gradient Boosting, and XGBoost.
• Model validation techniques, including cross-validation and train-test strategies.
• Hyperparameter tuning approaches, including grid search, random search, and Bayesian optimization.
• Data cleansing, transformation, and feature engineering.
• Good understanding of algorithms and data-structures fundamentals, including sorting, searching, and basic graph concepts.
• Ability to work independently and handle end-to-end problem-solving.
• Hands-on experience with Databricks, Apache Spark, or PySpark.
• Exposure to Healthcare Analytics domains including claims data, CMS reimbursement, population health, and Medicare Advantage.
• Experience building and deploying inference or prediction pipelines.
• Familiarity with ML lifecycle tools such as MLflow and Azure ML.
• Exposure to cloud platforms including Azure, AWS, or GCP.

Details

• Location: Chennai.
• Position based at the CDC2F - SEZ location.
• Mandatory 5-day return to office from one of four strategic office locations: Chennai, Coimbatore, Hyderabad, or Bangalore.
• Shift B support requires availability until 11 AM PST.