ML Ops Engineer

ML Ops Engineer – Cupertino, CA
One of The Select Groups’ top clients is looking for a Machine Learning Ops Engineer to develop and deploy ML models that power innovative and intuitive consumer-facing products. It is a long-term, hybrid contract going onsite in Cupertino, CA three times per week. We must work W2 for this position.

MUST HAVE SKILLS:

• 2 years of experience developing and deploying ML models that support customer-facing products.
• A strong background in large language models (LLM).
• Excellent communication and collaboration skills.
• Strong Python skills.
• Hands on involvement building services (open to a variety of tools including PyTorch, TensorFlow, Transformers, Kubernetes, Docker, LangChain, vectorDB).
• Experience with cloud platforms (i.e., AWS, GCP, or Azure).
• Proficiency with monitoring tools (i.e., Grafana)
• Experience with CI/CD (tools such as airflow, gitlab).
• Big Data management (i.e., Spark, Kafka).

EDUCATION/CERTIFICATIONS:

• Bachelor’s or Master’s degree in computer science, machine learning, or a related field.

DAY TO DAY:

• Model Management: Oversee the deployment, maintenance, and scaling of LLM, and services within our consumer-facing products.
• CI/CD: Build and maintain CI/CD pipelines to automate model train/test/deployment and scaling.
• Collaborative Integration: Partner with product developers, UX designers, and data scientists to ensure a seamless and intuitive integration of language models into our products.
• Continuous Monitoring: Build Dashboard and Regularly track model performance, ensuring consistent accuracy and reliability for consumers. Set up alerts and manage the type of monitoring needed.
• Feedback Integration: Develop strategies for collecting user feedback and refining the model for better alignment with consumer needs.
• Bias Mitigation: Proactively address and reduce potential biases in model predictions, ensuring our products are inclusive and fair.
• Infrastructure Management: Design and implement efficient data pipelines to support large language model training and inference.
• Documentation: Maintain comprehensive documentation covering model versions, deployment protocols, and performance metrics.
• Research & Development: Stay updated with the latest trends in large language models and MLOps, ensuring our consumer products remain at the forefront of innovation.