Senior Machine Learning Ops Engineer, SecurityScorecard

• 4-5+ years experience in MLOps / DevOps in the cloud (AWS, GCP, or Azure)
• Experience with Apache Spark and big data streaming infrastructure (data lakes, Snowflake, Databricks, S3)
• Production environment experience with Amazon Web Services (AWS) or equivalent
• Experience supporting data stores such as RDMBS (Postgres), KVS (Cassandra / ScyllaDB) and queues / streaming (Kafka)
• Skilled with Terraform, Git, Python, bash / shell scripting, and Docker containers
• Experienced with CI/CD processes (Jenkins, Ansible) and automated configuration tools (Terraform, Ansible, etc.)
• Experience setting up container orchestration (AWS ECS, Kubernetes / K8s)
• Skilled with dashboard creation and monitoring with tools such as Prometheus and DataDog
• Capable of planning out future infrastructure and projecting timelines
• Ability to work with our highly collaborative team
• Strong written and verbal communication skills
• Willingness to teach and mentor others
• If you're a problem solver, effective communicator, and enthusiastic about driving advancements in AI and ML in the security space, we want you on our team

Desirable

• In this role, you will collaborate with a cross functional team of ML engineers, data engineers and data science researchers
• You will collaborate with other experts to design, build, deploy, and operate production pipelines and microservice systems with a focus on MLOps best practices
• You will build and manage infrastructure including feature stores, data mesh and our AI platform, creating automation for training, delivery, and updating of our machine learning models
• Own and lead the creation, operation and maintenance of critical infrastructure projects and automation for the data science team to empower data science research and ML model delivery
• Train and mentor team members in applying best practices in operations and security
• Provide code reviews and feedback on Github pull requests
• Identify opportunities for technical and process improvement and implementation
• Knowledge and application of best practices such as immutable containers, Infrastructure as Code, stateless applications, and software observability
• Tune large-scale distributed system performance to achieve SLA metrics such as stability, uptime, scalability, and low latency while keeping costs under control
• Continuous improvement of CI/CD processes to automate builds and deployments
• Collaborate with scientists and engineers to understand KPIs and configure observability, monitoring, and alerting to support operations
• Setup Terraform / Kubernetes and associated tooling to support data pipelines, feature stores, data mesh and delivery of machine learning models
• Diagnose and correct networking issues or communicating problems clearly enough such that centralized IT teams can resolve
• Decompose system layer abstractions to investigate and determine root cause issues and resolve complex distributed system performance problems