Job Role:Senior Machine Learning Engineer
Location: NYC (Onsite)
Experience: 10 Years
Skills:
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
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🔹 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
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🔹 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
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🔹 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
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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
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Cloud & DevOps
• Experience working in cloud environments (AWS, Azure, or Google Cloud Platform)
• 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
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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
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