AI / ML Engineer

Intro: Senior AI/ML Engineer / Data Scientist role focused on traditional supervised learning and predictive analytics. The position requires hands-on development, training and deployment of machine learning models, strong Python and SQL skills, and the ability to lead and mentor a team. The posting emphasizes core data science fundamentals rather than GenAI/LLM-focused work. Key responsibilities: - Act as an SME and lead or manage team deliverables, make team-level decisions, and coordinate across multiple teams and stakeholders. - Translate stakeholder requirements into technical specifications and designs; lead design and implementation of AI-driven solutions aligned to business objectives. - Develop, train and deploy ML models for classification, regression and predictive analytics; build reusable, scalable Python-based data science pipelines and inference/prediction pipelines. - Perform data exploration, preprocessing, data cleansing and feature engineering on large/complex datasets. - Apply statistical techniques (hypothesis testing, distributions, variance analysis) and conduct model validation (cross-validation, A/B testing) and hyperparameter tuning to improve model performance. - Write optimized and scalable SQL queries for data extraction, transformation and analysis; ensure model performance monitoring, documentation and reproducibility. - Provide Shift B support (availability until 11 AM PST) and participate in knowledge sharing and mentoring. Required qualifications and experience: - Minimum experience: posting lists 5-10 years and a separate note specifies a minimum of 8 years in Data Science; candidate should meet the stricter 8+ years expectation for Data Science roles. - Educational qualification: 15 years full-time education. - Mandatory skills: Data Science, MySQL, Machine Learning, Python frameworks and strong SQL expertise. - Hands-on experience with common ML algorithms (logistic/linear regression, tree-based models including Random Forest, Gradient Boosting, XGBoost) and model validation/hyperparameter tuning techniques. Tools, domains and preferred experience: - Experience with Databricks / Apache Spark / PySpark; exposure to ML lifecycle tools such as MLflow and Azure ML and cloud platforms (Azure, AWS, GCP). - Exposure to Healthcare Analytics domain (claims data, CMS reimbursement, population health, Medicare Advantage) is highlighted. Location and working arrangement: - Based at Accenture CDC2F - SEZ location, Chennai. Return-to-office requirement: mandatory 5-day RTO from four strategic office locations (Chennai, Coimbatore, Hyderabad, Bangalore). Shift B support required (availability until 11 AM PST).