| MLOps Engineer at Austin, Texas, USA |
| Email: [email protected] |
| From: karthik abaka, Yochana [email protected] Reply to: [email protected] Job Title : MLOps Engineer Location : Austin, TX (Onsite from Day 1) We are seeking a skilled and motivated MLOps Engineer to join our dynamic team. The ideal candidate will be responsible for developing, implementing, and maintaining machine learning pipelines and infrastructure, ensuring the efficient and reliable deployment of ML models into production environments. You will collaborate closely with data scientists, software engineers, and IT professionals to optimize and automate the machine learning lifecycle. Key Responsibilities: Design, implement, and manage scalable ML model deployment pipelines. Automate the deployment process using CI/CD tools. Monitor, troubleshoot, and maintain deployed models in production. Develop and maintain infrastructure for data ingestion, processing, and storage. Optimize compute resources for cost and performance efficiency. Ensure high availability and scalability of ML infrastructure. Provide support for model retraining and updates. Implement monitoring and logging solutions for model performance and data quality. Optimize the performance of large language models (LLMs) in production environments. Implement techniques for efficient inference and fine-tuning of LLMs. Monitor and improve the scalability and latency of LLM deployments. Qualifications: Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Experience: Proven experience as an MLOps Engineer or in a similar role. Hands-on experience with ML model deployment and lifecycle management. Proficiency in programming languages such as Python, Java. Experience with ML frameworks such as TensorFlow, PyTorch. Familiarity with containerization and orchestration tools (Docker, Kubernetes). Experience with cloud platforms (AWS, GCP, Azure). Experience with large language models (LLMs) and their performance optimization. Keywords: continuous integration continuous deployment machine learning information technology Texas MLOps Engineer [email protected] |
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| Thu Jul 18 02:28:00 UTC 2024 |