Senior Machine Learning Infra/OPS Engineer

Key Skills

Apache Hadoop, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

The Security Analytics Engineering group is looking to hire a Sr. Data Scientist to help lead the development of cutting-edge machine learning models to better protect our users from malicious actors and improve our customer product experience. We are looking for a hands-on engineering leader with deep data science and software engineering expertise who can help architect and own the platform for deploying and tuning the machine learning models used to protect user authentication and security.

The teams focuses on using analytics, machine learning, and threat research on petabyte-scale data to deliver security value to millions of business users across thousands of businesses. The product portfolio is a part of the rapidly growing Carbon Black Cloud platform that delivers next-generation endpoint protection capabilities from the cloud. Now with the full resources of VMware, you have the opportunity to make an impact and build upon Carbon Black’s success.

Responsibilities

• Design and develop core components, infrastructure, and architecture of a machine learning platform for VMware CB SBU to create, train and deploy ML models
• Automate the day to day operational support for model training and model serving pipelinesCreate monitoring solutions that allow effective system accuracy, performance and enable troubleshooting of production ML models.
• Identify gaps and evaluate relevant tools and technologies as needed to improve processes and systems, leveraging open-source and cloud computing technologies to build effective solutions.
• Collaborate with data scientists, data engineers, product teams, and other key stakeholders and drive ML platform projects from conception to completion and production monitoring.

Skills

• 6+ years strong experience in large scale distributed systems, Data Engineering, MLOps, Machine Learning and Data Science areas
• Experience developing data pipelines and orchestrating the deployment of ML models for production ready systems .
• Must have working experience to MLOps tools such as KubeFlow, MLFlow, Metaflow, or Sagemaker
• Infrastructure as code - Terraform experience
• You can use AWS service and have a solid understanding of VPC, ALB/ELB, EC2, Route53, Kinesis, IAM, and other AWS concepts.
• Strong understanding of containerization (Docker) and container-orchestration systems like Kubernetes; experience with data workflow managers such as Airflow is a plus
• Experience with stream processing technology Kafka, Spark, Samza, Flink, etc.

Bonus Skills

• Working proficiency in SQL. Experience with big-data processing engines (Hive, Tez, Spark, Presto, Athena) is a plus.
• Familiarity with machine learning frameworks (like Keras or Tensor

Category : Engineering and Technology

Subcategory: Software Engineering

Experience: Manager and Professional

Full Time/ Part Time: Full Time

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