What are the responsibilities and job description for the Databricks Architect – MLOps (Banking / Model Risk Focus) position at ISite Technologies Inc?
Job Role: Databricks Architect – MLOps (Banking / Model Risk Focus)
Experience: 12
Location: Nyc
Note: Mlops Implementation
Skills:
We are seeking a Databricks Architect with deep MLOps expertise to lead the design and implementation of scalable machine learning platforms within a banking environment. This role will focus on building production-grade ML pipelines, governance frameworks, and model lifecycle management aligned with model risk management (MRM) standards.
Key Responsibilities:
• Architect and implement end-to-end MLOps frameworks on Databricks
• Design scalable ML pipelines using:
o Databricks Workflows
o MLflow (experiment tracking, model registry, deployment)
o Unity Catalog (governance, lineage, access control)
• Build and operationalize:
o CI/CD pipelines for ML models
o Automated model training, validation, and deployment workflows
• Establish model monitoring and observability (drift, performance, bias)
• Implement governance controls aligned with banking / regulatory requirements
• Partner with data science, risk, and engineering teams to productionize models
• Define best practices for feature engineering, versioning, and reproducibility.
Qualifications we seek in you!
Required Qualifications
• Experience in data/ML engineering or architecture
• Hands-on Databricks experience
• Strong expertise in MLOps frameworks and production ML systems
• Deep experience with:
o MLflow
o Python (PySpark, Pandas, scikit-learn)
o Spark-based data processing
• Experience designing enterprise-grade data platforms (lakehouse architecture)
• Proven ability to deploy ML models into production environments
Preferred Qualifications/ Skills
• Experience in banking or financial services.
• Strong understanding of Model Risk Management (MRM), including:
o Model validation workflows
o Auditability and documentation standards
o Regulatory expectations (SR 11-7, etc.)
• Familiarity with:
o Feature stores (Databricks Feature Store)
o Real-time / batch inference patterns
o Data governance and lineage tracking