Overview
On Site
$65 - $70
Contract - W2
Contract - Independent
Contract - 12 month(s)
10% Travel
Skills
Python
PYSPARK
Databricks
MLflow (Tracking/Registry/Deployment)
Feature Store
Point-in-Time (PIT) Feature Engineering
ML Pipelines
Automated Feedback Loops
Data Drift Detection
Model Drift Detection
Data Quality Monitoring
Hyperopt/Ray Tune
Delta Lake
Spark
CI/CD for ML
Cloud Data Engineering.
Job Details
Job Summary (Senior MLOps / ML Infrastructure Engineer)
- Design, build, and maintain scalable ML infrastructure and automated machine learning (ML) pipelines, with a strong focus on PySpark and Databricks.
- Develop and implement Point-in-Time (PIT) feature engineering pipelines and feedback loops for continuous model improvement.
- Manage end-to-end model monitoring systems to detect data drift, model drift, and data quality issues.
- Oversee the full ML model lifecycle using MLflow (including Tracking, Registry, and Deployment).
- Build and maintain large-scale data processing pipelines for feature engineering and ensure data quality.
- Manage Feature Store operations, including feature versioning and governance.
- Run large-scale hyperparameter tuning using tools like Hyperopt and Ray Tune.
- Collaborate closely with Data Scientists to productionize and deploy new machine learning models.
- Ensure ML systems follow best practices for CI/CD automation (preferred).
- Utilize advanced Databricks features (Jobs, Workflows, Feature Store, Unity Catalog) for robust ML system management.
- (Preferred) Work with Spark Structured Streaming and model calibration techniques such as Isotonic Regression.
Required Experience:
5+ years in MLOps, ML Engineering, or Data Engineering with a strong ML focus.
Expertise in PySpark, Databricks, MLflow, Feature Store, hyperparameter tuning, and model monitoring.
- Design, build, and maintain scalable ML infrastructure and automated machine learning (ML) pipelines, with a strong focus on PySpark and Databricks.
- Develop and implement Point-in-Time (PIT) feature engineering pipelines and feedback loops for continuous model improvement.
- Manage end-to-end model monitoring systems to detect data drift, model drift, and data quality issues.
- Oversee the full ML model lifecycle using MLflow (including Tracking, Registry, and Deployment).
- Build and maintain large-scale data processing pipelines for feature engineering and ensure data quality.
- Manage Feature Store operations, including feature versioning and governance.
- Run large-scale hyperparameter tuning using tools like Hyperopt and Ray Tune.
- Collaborate closely with Data Scientists to productionize and deploy new machine learning models.
- Ensure ML systems follow best practices for CI/CD automation (preferred).
- Utilize advanced Databricks features (Jobs, Workflows, Feature Store, Unity Catalog) for robust ML system management.
- (Preferred) Work with Spark Structured Streaming and model calibration techniques such as Isotonic Regression.
Required Experience:
5+ years in MLOps, ML Engineering, or Data Engineering with a strong ML focus.
Expertise in PySpark, Databricks, MLflow, Feature Store, hyperparameter tuning, and model monitoring.
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