Job Title: Senior Machine Learning Engineer (Cloud & Data Platform)

Experience: 10 Years

Work Location: New York 

Skills:

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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