Core ML/ ML Ops/ Data Engineer

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