Staff MLOps Engineer
August 14
Description
• Design, implement, and maintain robust MLOps platforms and tooling for both batch and streaming ML pipelines. • Develop and manage monitoring and observability solutions for ML systems. • Lead DevOps practices, including CI/CD pipelines and Infrastructure as Code (IaC). • Architect and implement cloud-based solutions on AWS. • Collaborate with ML Engineers and Data Scientists to develop, train, and deploy machine learning models. • Engage in feature engineering and model optimization to improve ML system performance. • Participate in the full ML lifecycle, from data preparation to model deployment and monitoring. • Optimize and refactor existing systems for improved performance and reliability. • Drive technical initiatives and best practices in both MLOps and ML Engineering.
Requirements
• Strong Python Proficiency: Excellent skills for developing, deploying, and maintaining our machine learning systems. • Language Versatility: Experience with statically-typed or JVM languages. Willingness to learn Scala is highly desirable. • Cloud Engineering Skills: Extensive experience with Cloud Platforms & Services, ideally AWS (e.g., Lambda, ECS, ECR, CloudWatch, MSK, SNS, SQS). • Infrastructure as Code: Proficiency in IaC, particularly Terraform. • Kubernetes Expertise: Strong hands-on experience with managing clusters and deploying services. • Data Orchestration: Experience with ML orchestration tools (e.g., Flyte, Airflow, Kubeflow, Luigi, or Prefect). • CI/CD: Expertise in pipelines, especially GitHub Actions and Jenkins. • Networking: Knowledge of concepts and implementation. • Streaming: Experience with Kafka and other streaming technologies. • ML Monitoring: Familiarity with observability tools (e.g., Arize AI, Weights and Biases). • NLP/LLMs: Experience with NLP, LLMs, and RAG systems in production, or strong desire to learn. • CLI & Shell Scripting: Proficiency in scripting and command-line tools. • APIs: Experience with deploying and managing production APIs. • Software Engineering Best-Practices: Knowledge of industry standards and practices.
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