AI / ML Engineer

Intro: Senior-level AI/ML Engineer / Data Scientist role responsible for designing, building and delivering production-ready machine learning solutions. The role focuses on traditional ML and predictive analytics (classification, regression, statistical modeling) using Python and SQL; it may also involve applying GenAI models and other AI techniques (deep learning, neural networks, chatbots, image processing) but is explicitly not GenAI/NLP-focused. Key responsibilities: - Act as an SME and lead a team: make team decisions, mentor and facilitate knowledge sharing, and coordinate across multiple teams and stakeholders. - Translate stakeholder requirements into technical specifications, design AI-driven solutions aligned to business objectives, and monitor project progress. - Develop, train, validate and deploy ML models for classification, regression and predictive analytics use cases; build reusable, scalable Python-based data science pipelines and inference/prediction pipelines. - Perform data exploration, preprocessing, cleansing and feature engineering on large/complex datasets; write optimized SQL for extraction, transformation and analysis. - Apply statistical techniques (hypothesis testing, distributions, variance analysis) and robust model validation frameworks (cross-validation, A/B testing, train-test strategies). - Conduct hyperparameter tuning (grid search, random search, Bayesian optimization) and ensure model performance monitoring, documentation and reproducibility. - Provide Shift B support availability until 11 AM PST and comply with mandatory 5-day return-to-office from strategic office locations. Mandatory / required qualifications and experience: - Must-have skills listed: Data Science, MySQL, Machine Learning, Python frameworks; strong expertise in Python and SQL. - Hands-on experience with ML algorithms (Logistic Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost) and model validation techniques. - Proven ability to build and deploy inference/prediction pipelines; experience with Databricks / Apache Spark / PySpark. - Solid grounding in statistics and probability, and familiarity with algorithms and data structures fundamentals. - Exposure to ML lifecycle tools (MLflow, Azure ML) and cloud platforms (Azure, AWS, GCP). - Domain exposure: Healthcare Analytics (claims data, CMS reimbursement, population health, Medicare Advantage) is noted. Notes and constraints: - The posting lists experience ranges in multiple places: header indicates 5-10 years and a minimum of 5 years is stated; an additional note states the candidate should have minimum 8 years of experience in Data Science. These conflicting statements are preserved as provided. - Educational requirement: 15 years of full-time education. - Location: role is based at Accenture CDC2F - SEZ location with mandatory on-site attendance at strategic offices (Chennai, Coimbatore, Hyderabad, Bangalore).