SWITS DIGITAL Private Limited
Core ML/ ML Ops/ Data Engineer
SWITS DIGITAL Private Limited, San Jose, California, United States, 95199
Job Title
Job Title:
Core ML / MLOps / Data Engineer Experience:
4–7 Years Location:
California Employment Type:
Contract - W2 Job Summary
The ideal candidate will have strong expertise in machine learning model development, deployment, and lifecycle management, along with proficiency in CI/CD pipelines, containerization, orchestration, and cloud data platforms. You will work closely with data scientists, analysts, and business stakeholders to build scalable ML solutions and ensure seamless operationalization. Key Responsibilities
Design, build, and deploy machine learning models into production environments. Implement and manage CI/CD pipelines for ML workflows. Containerize ML applications using Docker and orchestrate with Kubernetes. Work with Snowflake or Databricks for scalable data engineering and model training. Ensure robust monitoring, logging, and versioning of ML models and data pipelines. Collaborate with data scientists and business teams to translate requirements into deployable solutions. Optimize performance, scalability, and cost-efficiency of ML systems in production. Troubleshoot and resolve issues related to model performance, pipelines, and infrastructure. Required Skills & Experience
4–7 years of experience in ML engineering, MLOps, or Data Engineering. Strong experience in ML model development, deployment, and operationalization. Expertise in CI/CD tools (GitHub Actions, Jenkins, GitLab CI, or equivalent). Hands-on experience with Docker and Kubernetes. Proficiency in Snowflake or Databricks for large-scale data processing and analytics. Strong programming skills in Python (preferred) or similar languages. Knowledge of ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.). Familiarity with cloud platforms (AWS, Azure, or GCP) is a plus. Strong problem-solving, debugging, and optimization skills.
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Job Title:
Core ML / MLOps / Data Engineer Experience:
4–7 Years Location:
California Employment Type:
Contract - W2 Job Summary
The ideal candidate will have strong expertise in machine learning model development, deployment, and lifecycle management, along with proficiency in CI/CD pipelines, containerization, orchestration, and cloud data platforms. You will work closely with data scientists, analysts, and business stakeholders to build scalable ML solutions and ensure seamless operationalization. Key Responsibilities
Design, build, and deploy machine learning models into production environments. Implement and manage CI/CD pipelines for ML workflows. Containerize ML applications using Docker and orchestrate with Kubernetes. Work with Snowflake or Databricks for scalable data engineering and model training. Ensure robust monitoring, logging, and versioning of ML models and data pipelines. Collaborate with data scientists and business teams to translate requirements into deployable solutions. Optimize performance, scalability, and cost-efficiency of ML systems in production. Troubleshoot and resolve issues related to model performance, pipelines, and infrastructure. Required Skills & Experience
4–7 years of experience in ML engineering, MLOps, or Data Engineering. Strong experience in ML model development, deployment, and operationalization. Expertise in CI/CD tools (GitHub Actions, Jenkins, GitLab CI, or equivalent). Hands-on experience with Docker and Kubernetes. Proficiency in Snowflake or Databricks for large-scale data processing and analytics. Strong programming skills in Python (preferred) or similar languages. Knowledge of ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.). Familiarity with cloud platforms (AWS, Azure, or GCP) is a plus. Strong problem-solving, debugging, and optimization skills.
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