ML Engineer – Databricks & MLflow (Hybrid – NYC)

About the Role

We are hiring a hands-on ML Engineer to work with a leading financial client on building and scaling production-grade machine learning systems.

This role is focused on Databricks + MLflow-based MLOps pipelines, and requires strong experience in deploying and managing ML models in enterprise environments.

Location

New York City (Hybrid – 2–3 days onsite, 3 preferred)

Employment Type

Contract (C2C / W2)

Key Responsibilities

• Design, build, and maintain end-to-end ML pipelines using Databricks and MLflow
• Develop and deploy models with MLflow (experiment tracking, model registry, versioning)
• Implement MLOps frameworks for CI/CD, monitoring, and model lifecycle management
• Work with large-scale data using PySpark, Delta Lake, and Databricks
• Collaborate with data scientists and business teams to productionize ML solutions
• Ensure scalability, reliability, and performance of ML systems in production

Required Skills

• 6–8+ years of experience in Machine Learning / Data Science / MLOps
• Strong hands-on experience with:
  • Databricks (must-have)
  • MLflow (must-have, 3+ years preferred)
• Experience with PySpark / Python for large-scale data processing
• Strong understanding of ML lifecycle (training, deployment, monitoring)
• Experience with CI/CD pipelines and model deployment frameworks
• Familiarity with Delta Lake / feature stores / model monitoring

Mandatory Requirement

• Databricks Certification (required)

Good to Have

• Experience in Banking / Financial Services
• Exposure to real-time ML systems or streaming pipelines
• Experience with cloud platforms (AWS / Azure)