Staff MLOps Engineer, Bazaarvoice

• 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
• Passionate about building developer-friendly platforms and tools
• Thrives in a terminal-based development environment
• Enthusiastic about creating production-grade, robust, reliable, and performant systems
• Not afraid to dive into and improve complex existing solutions
• Team player who works well with ML Engineers, Data Scientists, and management
• Strong technical mentoring skills
• Excellent problem-solving and communication skills

Desirable

• You’ll work closely with a team of engineers to create a platform on top of that data that will be leveraged by virtually every other product and system we have built or will build in the future
• You’ll be responsible for building and maintaining the infrastructure and tooling that enables our ML Engineers and Data Scientists to focus on model development and feature engineering
• 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