🔹 Job Title
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)
