PROCESSING APPLICATION
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Job Requirements of ML Data Engineer:
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Employment Type:
Full-Time
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Location:
San Francisco, CA (Onsite)
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ML Data Engineer
Job Summary:
We are seeking a highly skilled Data/ML Engineer to design, build, and optimize data pipelines and machine learning infrastructure. You will work closely with data scientists, analysts, and software engineers to enable scalable machine learning models and real-time data processing. This role requires expertise in big data processing, cloud technologies, and MLOps best practices to support AI-driven applications.
Key Responsibilities:
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Design, develop, and maintain ETL/ELT pipelines for structured and unstructured data.
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Implement scalable data architectures using cloud platforms (AWS, GCP, Azure).
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Develop and optimize machine learning pipelines for training, validation, and deployment.
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Work closely with data scientists to productionize ML models using MLflow, TensorFlow, PyTorch, or Scikit-learn.
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Implement MLOps best practices, including CI/CD pipelines for model deployment and monitoring.
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Optimize data storage and retrieval using data lakes, warehouses (Snowflake, Redshift, BigQuery), and NoSQL databases.
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Develop real-time data streaming solutions using Kafka, Kinesis, or Apache Flink.
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Ensure data quality, governance, and compliance with industry standards.
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Automate workflows using Airflow, Prefect, or Dagster.
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Monitor model performance and ensure retraining pipelines are in place.
Required Skills & Qualifications:
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3-7 years of experience in Data Engineering, ML Engineering, or a related field.
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Strong programming skills in Python and SQL (experience with Scala or Java is a plus).
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Hands-on experience with big data processing frameworks (Spark, Hadoop, Dask).
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Expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-learn).
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Experience with containerization (Docker, Kubernetes) for model deployment.
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Familiarity with feature engineering, feature stores, and vector databases (Feast, Pinecone).
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Strong understanding of data pipelines, batch & streaming data processing.
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Knowledge of MLOps tools (Kubeflow, MLflow, Sagemaker, Vertex AI).
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Proficiency in cloud services such as AWS (S3, Lambda, SageMaker), GCP (BigQuery, Vertex AI), or Azure (Synapse, ML Studio).
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Experience with monitoring and logging tools (Prometheus, Grafana, ELK Stack).
Nice to Have:
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Experience working in Retail, Finance, Healthcare, or E-commerce domains.
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Exposure to A/B testing, recommendation systems, or NLP applications.
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Understanding of data privacy regulations (GDPR, CCPA).