Senior Machine Learning Engineer

We're Nagarro.

We are a digital product engineering company that is scaling in a big way We build products, services, and experiences that inspire, excite, and delight. We work at scale — across all devices and digital mediums, and our people exist everywhere in the world experts across 39 countries, to be exact). Our work culture is dynamic and non-hierarchical. We're looking for great new colleagues. That's where you come in

By this point in your career, it is not just about the tech you know or how well you can code. It is about what more you want to do with that knowledge. Can you help your teammates proceed in the right direction? Can you tackle the challenges our clients face while always looking to take our solutions one step further to succeed at an even higher level? Yes? You may be ready to join us.

We are seeking an experienced Senior Machine Learning Engineer with a strong background in building, deploying, and maintaining end-to-end ML models—particularly on Google Cloud Platform (GCP) using Vertex AI and related services. You'll be part of a team that designs and implements scalable, production-ready ML systems powering impactful business decisions.

Key Responsibilities

• Design, develop, and deploy ML models using Python and frameworks such as Scikit-learn, XGBoost/CatBoost, Pandas, NumPy, TensorFlow, and Keras.
• Fetch, clean, and prepare data from BigQuery and other structured/unstructured data sources.
• Build and maintain real-time and batch-based ML pipelines using Vertex AI, Cloud Run, and Vertex Pipelines.
• Apply expertise in Regression, Classification, Forecasting, Unsupervised Learning, Graph Data, GIS Data, and Natural Language Processing (NLP).
• Perform exploratory data analysis (EDA), feature engineering, and statistical testing to evaluate model performance and significance.
• Execute hyperparameter tuning and leverage tools for optimizing ML performance.
• Ensure model robustness, explainability, and bias mitigation, especially in regulated environments.
• Develop and implement evaluation metrics to measure ML model effectiveness.
• Stay up-to-date with emerging trends and best practices in AI, ML, and MLOps.

• Extensive hands-on experience designing, building, and deploying end-to-end machine learning models on Google Cloud Platform (GCP) using Vertex AI and related tools.
• Strong programming skills in Python with proficiency in frameworks such as Scikit-learn, XGBoost/CatBoost, TensorFlow, Keras, Pandas, and NumPy.
• Solid understanding of core machine learning techniques, including regression, classification, forecasting, unsupervised learning, graph data, GIS data, and natural language processing (NLP).
• Proven experience deploying and maintaining ML models in production for both real-time and batch-based use cases.
• Hands-on expertise in exploratory data analysis (EDA), feature engineering, statistical testing, and hyperparameter tuning.
• Familiarity with MLOps best practices and cloud-native tools such as Vertex Pipelines, Cloud Functions, Cloud Run, BigQuery, AutoML, DocAI, Cloud Build, and Artifact Registry.
• Experience working in regulated industries such as banking, financial services, or insurance (BFSI), with an emphasis on model explainability, bias mitigation, and compliance.
• Exposure to reinforcement learning, with or without human feedback, for continuous model optimization.
• Passion for staying up-to-date with the latest trends and advancements in AI and machine learning.

Good to Have

• Certifications:
  • Google Certified Professional Machine Learning Engineer
  • Machine Learning / Data Science / Statistics coursework or certification
• Knowledge of BFSI / NBFC ecosystems
• Solid understanding of probability and statistical methods