Senior Machine Learning Engineer – GCP, Vertex AI
Employment Information
Job Description
Senior Machine Learning Engineer at Nagarro developing and deploying ML models using GCP and Vertex AI. Collaborating with a team to deliver scalable, production-ready ML systems.
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.
Requirements:
- 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.
Benefits:
- Health insurance
- Flexible work arrangements
- Professional development opportunities
- Remote work options
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