ML Engineer

Design and implement robust machine learning models using modern frameworks and libraries. Collaborate with data scientists, engineers, and stakeholders to translate business requirements into technical solutions. Optimize and deploy models using tools like TensorFlow Serving, TorchServe, ONNX, and TensorRT. Build and manage ML pipelines using MLflow, Kubeflow, and AWS ML pipelines. Work with large-scale data using PySpark and integrate models into production environments. Monitor model performance and retrain as needed to ensure accuracy and efficiency. Collaborate with cross-functional teams to integrate AI solutions into scalable products Ensure best practices in data engineering and contribute to architectural decisions Contribute to the mentoring and development of junior team members. Support senior team members in identifying and addressing data science opportunities. SKILLS Must have Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using PySpark. Solid understanding of software engineering principles and version control (e.g., Git). Excellent problem-solving skills and ability to work independently or in a team. Typically, 6+ years of relevant work experience in industry, with a minimum of 2+ years in a similar role. Proficiencies in data cleansing, exploratory data analysis, and data visualization Continuous learner that stays abreast with industry knowledge and technology Nice to have Previous experience in Capital Markets software development projects .