AI/ML Engineer/Manager

As an AI/ML Manager at our Pune location, you will be responsible for leading the development of machine learning proof of concepts (PoCs) and demos using structured/tabular data for various use cases like forecasting, risk scoring, churn prediction, and optimization. Your role will involve collaborating with sales engineering teams to understand client requirements and presenting ML solutions during pre-sales calls and technical workshops. You will be expected to build ML workflows using tools such as SageMaker, Azure ML, or Databricks ML, managing training, tuning, evaluation, and model packaging. Applying supervised, unsupervised, and semi-supervised techniques like XGBoost, CatBoost, k-Means, PCA, and time-series models will be a key part of your responsibilities. Working closely with data engineering teams, you will define data ingestion, preprocessing, and feature engineering pipelines using Python, Spark, and cloud-native tools. Packaging and documenting ML assets for scalability and transition into delivery teams post-demo will be essential. Staying updated with the latest best practices in ML explainability, model performance monitoring, and MLOps practices is also expected. Participation in internal knowledge sharing, tooling evaluation, and continuous improvement of lab processes are additional aspects of this role. To qualify for this position, you should have at least 8+ years of experience in developing and deploying classical machine learning models in production or PoC environments. Strong hands-on experience with Python, pandas, scikit-learn, and ML libraries like XGBoost, CatBoost, LightGBM is required. Familiarity with cloud-based ML environments such as AWS SageMaker, Azure ML, or Databricks is preferred. A solid understanding of feature engineering, model tuning, cross-validation, and error analysis is necessary. Experience with unsupervised learning, clustering, anomaly detection, and dimensionality reduction techniques will be beneficial. You should be comfortable presenting models and insights to both technical and non-technical stakeholders during pre-sales engagements. Working knowledge of MLOps concepts, including model versioning, deployment automation, and drift detection, will be an advantage. If you are interested in this opportunity, please apply or share your resume at kanika.garg@austere.co.in.,