Senior Machine Learning Engineer

Job Role: Senior Machine learning Engineer/Data scientist Job Location: NYC Experience: 10years Job Description: Machine Learning Engineering • Design, develop, and deploy scalable machine learning models using modern frameworks (e.g., PyTorch) • Re-engineer and optimize legacy models into efficient, production-grade implementations • Improve model performance, scalability, and reproducibility • Support model validation, benchmarking, and certification processes • Ensure full traceability and documentation of model logic and outputs \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ 🔹 Data Platform & Pipeline Engineering • Design and optimize distributed data pipelines using Spark-based platforms (e.g., Databricks) • Build and refactor ETL/ELT workflows for performance and scalability • Implement data models within modern cloud data warehouses (e.g., Snowflake) • Apply best practices for cloud-native data architecture • Standardize reusable utilities and frameworks for analytics workflows \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ 🔹 Cloud Migration & Modernization • Participate in migration of on-prem or legacy analytics platforms to cloud ecosystems • Refactor existing codebases to align with modern engineering and DevOps standards • Leverage cloud compute capabilities (including GPU acceleration where applicable) • Support scheduling and orchestration of data and ML workflows \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ 🔹 Testing, Validation & Governance • Conduct rigorous testing and validation to ensure data and model accuracy • Perform parallel runs and benchmarking when modernizing systems • Collaborate with governance, risk, and compliance stakeholders • Maintain high standards of documentation and reproducibility \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Required Qualifications Technical Skills • Strong programming skills in Python • Hands-on experience with PyTorch (or similar deep learning frameworks) • Expertise in Spark-based data processing (Databricks preferred) • Strong SQL skills • Experience working with cloud data warehouses such as Snowflake • Experience building and optimizing ETL/ELT pipelines • Familiarity with distributed computing and performance tuning \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Cloud & DevOps • Experience working in cloud environments (AWS, Azure, or GCP) • Understanding of workflow orchestration tools (e.g., Airflow, native platform schedulers) • Version control and CI/CD practices for ML pipelines • Exposure to containerization and scalable deployment patterns \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Preferred Qualifications • Experience modernizing legacy codebases (C++, R, or similar) • Experience in regulated industries (Financial Services, Banking, Insurance, etc.) • GPU optimization experience • Knowledge of model risk management or model validation frameworks • Experience supporting large-scale data transformation initiatives