AIML Manager

Role & responsibilities

• Lead the development of machine learning PoCs and demos using structured/tabular data for use cases such as forecasting, risk scoring, churn prediction, and optimization.
• Collaborate with sales engineering teams to understand client needs and present ML solutions during pre-sales calls and technical workshops.
• Build ML workflows using tools such as SageMaker, Azure ML, or Databricks ML and manage training, tuning, evaluation, and model packaging.
• Apply supervised, unsupervised, and semi-supervised techniques such as XGBoost, CatBoost, k-Means, PCA, time-series models, and more.
• Work with data engineering teams to define data ingestion, preprocessing, and feature engineering pipelines using Python, Spark, and cloud-native tools.
• Package and document ML assets so they can be scaled or transitioned into delivery teams post-demo.
• Stay current with best practices in ML explainability, model performance monitoring, and MLOps practices.

Participate in internal knowledge sharing, tooling evaluation, and continuous improvement of lab processes.

Preferred candidate profile

• 8+ years of experience developing and deploying classical machine learning models in production or PoC environments.
• Strong hands-on experience with Python, pandas, scikit-learn, and ML libraries such as XGBoost, CatBoost, LightGBM, etc.
• Familiarity with cloud-based ML environments such as AWS SageMaker, Azure ML, or Databricks.
• Solid understanding of feature engineering, model tuning, cross-validation, and error analysis.
• Experience with unsupervised learning, clustering, anomaly detection, and dimensionality reduction techniques.
• Comfortable presenting models and insights to technical and non-technical stakeholders during pre-sales engagements.
• Working knowledge of MLOps concepts, including model versioning, deployment automation, and drift detection.