What are the responsibilities and job description for the Urgent Needed - MLOps Engineer - Concord, CA position at Jobs via Dice?
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Hi,
Our client is looking for MLOps Engineer for Concord, CA. If you are looking for a job change, please let me know.
Job Title: MLOps Engineer
Location: Concord, CA
12 Months of Contract Role
Job Description:
We are seeking a skilled and passionate Machine Learning Engineer to join our team and help design, build, and optimize scalable ML solutions. The ideal candidate will have deep expertise in the Python ML ecosystem and experience developing and managing robust model training pipelines.
Key Responsibilities:
Hi,
Our client is looking for MLOps Engineer for Concord, CA. If you are looking for a job change, please let me know.
Job Title: MLOps Engineer
Location: Concord, CA
12 Months of Contract Role
Job Description:
We are seeking a skilled and passionate Machine Learning Engineer to join our team and help design, build, and optimize scalable ML solutions. The ideal candidate will have deep expertise in the Python ML ecosystem and experience developing and managing robust model training pipelines.
Key Responsibilities:
- Design and implement scalable machine learning models and training workflows using PyTorch or TensorFlow.
- Develop and maintain end-to-end ML pipelines, from data preprocessing to model deployment.
- Leverage the Python ecosystem (NumPy, pandas, scikit-learn, spaCy, NLTK, Hugging Face Transformers) for feature engineering, model development, and evaluation.
- Manage and track machine learning experiments using MLflow, ensuring reproducibility, versioning, and lifecycle management.
- Collaborate with data scientists, software engineers, and product teams to deploy ML models into production environments.
- Continuously optimize models and pipelines for performance, scalability, and accuracy.
- Strong proficiency in Python and experience with ML libraries such as NumPy, pandas, scikit-learn, spaCy, NLTK, and Hugging Face Transformers.
- Hands-on experience building scalable ML training pipelines using PyTorch or TensorFlow.
- Experience with MLflow or similar tools for experiment tracking and model lifecycle management.
- Solid understanding of machine learning fundamentals, including supervised and unsupervised learning, NLP, and model evaluation techniques.
- Experience with version control (e.g., Git) and working in collaborative, agile teams.
- Familiarity with cloud platforms (AWS, Google Cloud Platform, or Azure) and containerization tools (Docker, Kubernetes).
- Knowledge of MLOps practices and tools for model deployment and monitoring.
- Experience in deploying NLP-based models into production environments.