ML Ops Developer

Title:

Experience:

Location:

Job Overview:

We are looking for a highly skilled ML Ops Engineer with 5+ years of experience to build and maintain ML pipelines, ensure efficient model deployment, and optimize machine learning workflows. The ideal candidate will have expertise in Python, ML concepts, PySpark/Spark, GCP services, SQL/BigQuery, and DevOps to drive scalable ML solutions.

Key Responsibilities:

Design, develop, and maintain ML pipelines for training, validation, and deployment.

Implement and optimize ML models using Python and scalable frameworks like PySpark/Spark.

Leverage GCP services (BigQuery, Vertex AI, Cloud Functions, Pub/Sub) for ML operations and data processing.

Manage model versioning, monitoring, and retraining workflows to ensure high performance and accuracy.

Develop and maintain CI/CD pipelines for ML model deployment and integration using DevOps principles.

Ensure data integrity and scalability using SQL and BigQuery for large-scale processing.

Collaborate with data engineers and scientists to integrate ML models into production systems.

Optimize model performance through monitoring, logging, and retraining strategies.

Required Skills:

Strong Python programming expertise with ML libraries (Scikit-learn, TensorFlow, PyTorch).

Experience with distributed computing using PySpark/Spark.

Proficiency in GCP services, including BigQuery, Dataflow, and Vertex AI.

Solid understanding of ML concepts, feature engineering, and model deployment.

Expertise in SQL for querying and managing large datasets.

DevOps knowledge with CI/CD pipelines, Docker, Kubernetes, and GitHub Actions.

Experience working with cloud-based ML deployments.

Preferred Qualifications:

Familiarity with Airflow for workflow automation.

Experience with MLOps tools like MLflow or Kubeflow.

Knowledge of monitoring tools (Prometheus, Grafana) for ML applications.