Senior AI Engineer

You have 5 years of hands-on experience in building and deploying AI solutions, preferably in fintech or financial services. Your strong coding skills in Python and familiarity with libraries like TensorFlow, PyTorch, scikit-learn, XGBoost, etc., will be beneficial for this role. Additionally, your experience with NLP, time series forecasting, anomaly detection, or graph ML relevant to fintech applications will be highly valued. Key Responsibilities: - Solid understanding of MLOps concepts such as model versioning, deployment, and monitoring. - Proficient in working with cloud platforms (AWS/GCP/Azure) and ML services like SageMaker, Vertex AI. - Knowledge of Docker, Kubernetes, or other containerization tools for model deployment is a plus. - Comfortable handling large-scale structured and unstructured datasets, with experience in LLMs or generative AI for fintech-specific use cases. - Exposure to RegTech, risk modeling, or algorithmic trading. - Participation in publications, GitHub contributions, or Kaggle competitions. - Familiarity with SQL/NoSQL databases and data pipelines like Airflow, Spark, etc. Qualifications Required: - 5 years of hands-on experience in building and deploying AI solutions, preferably in fintech or financial services. - Strong coding skills in Python and familiarity with libraries like TensorFlow, PyTorch, scikit-learn, XGBoost, etc. - Experience with NLP, time series forecasting, anomaly detection, or graph ML relevant to fintech applications. - Solid understanding of MLOps concepts such as model versioning, deployment, and monitoring. - Experience working with cloud platforms (AWS/GCP/Azure) and ML services like SageMaker, Vertex AI. - Familiarity with Docker, Kubernetes, or other containerization tools for model deployment is a plus. - Comfortable working with large-scale structured and unstructured datasets, with experience in LLMs or generative AI for fintech-specific use cases. - Exposure to RegTech, risk modeling, or algorithmic trading. - Participation in publications, GitHub contributions, or Kaggle competitions. - Familiarity with SQL/NoSQL databases and data pipelines like Airflow, Spark, etc.,