Sr. AI/ML Engineer for Dental Services Client
Long Term Contract with Potential to Convert
Onsite in Irvine, CA
Responsibilities:
- Lead End-to-End ML Lifecycle: Oversee the entire machine learning lifecycle, from data preparation and model training to deployment and product ionization, ensuring high-performance, scalable, and robust models.
- Data Preparation and Feature Engineering: Prepare, clean, and engineer data, ensuring high quality for model input and adhering to best practices in feature engineering and data selection.
- Model Development & Training: Develop and fine-tune ML models, using best practices in model evaluation, hyperparameter tuning, and optimization to ensure accuracy and efficiency.
- Model Deployment & Monitoring: Lead the deployment of models into production environments, establish monitoring and versioning systems, and continuously optimize for performance and stability.
- Mentor and Guide Junior Engineers: Provide technical mentorship to junior ML/AI engineers, promoting best practices in model development, code review, and problem-solving within a collaborative team environment.
- Collaboration with Cross-Functional Teams: Partner closely with data engineering, product, and DevOps teams to align on objectives, ensure data pipelines are ML-ready, and integrate models into product features.
- Drive Innovation and Model Performance: Stay current with and experiment with the latest ML techniques and technologies to enhance model accuracy, efficiency, and reliability.
- Documentation & Compliance: Maintain comprehensive documentation of model development and deployment processes, ensuring adherence to data governance, regulatory requirements, and compliance standards.
Required Skills:
- Educational Background: Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
- Extensive ML Experience: 5+ years of hands-on experience in machine learning, with expertise in model development, training, deployment, and lifecycle management, including supervised and unsupervised learning.
- Strong experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Advanced programming skills in Python, including data manipulation libraries like Pandas and NumPy.
- Familiarity with model versioning and monitoring tools (e.g., MLflow, Kubeflow) and experience with cloud platforms (e.g., AWS, Azure, or GCP) for ML model deployment.
- Model Productionization Skills: Proven ability to deploy models in production, with experience in model packaging, version control, and monitoring.
- MLOps Experience: Familiarity with MLOps practices, including CI/CD pipelines, automation, and integration of ML models in production environments.
- Analytical and Problem-Solving Skills: Strong analytical skills, with the ability to design solutions for complex business and technical challenges.
- Communication Skills: Excellent written and verbal communication skills, with the ability to convey technical concepts to both technical and non-technical stakeholders.
