Location : ,
Location: Irving, TX or Richardson, TX
Job Description
Day to Day: This person will take the base code model that has been developed by the Data Science team and will be responsible to scale it, deploy it in a more reusable way, and manage the pipeline
Position Summary
As a Lead ML Ops Engineer, you will work on the Retail side of the company and drive the design and implementation of functionality related to the end-to-end ML/AI and Feature lifecycle management on Azure/Google Cloud Platform, leveraging and integrating the cloud native services with other standard operational and automation tools. Once this has been established, you will develop models to support Engineering projects. EML Ops is the expectation of the role. You will be developing models that can help from an Engineering perspective
You will also be responsible for guiding more JR members of the team. 70% individual contributor, 30% reviewing JR-level engineers
Required:
• Python & SQL for scripting & programming
• Knowledge of ML & Ops Engineering
• Open source (but they use Python on Azure Kubernetes)
• ML Ops
• Kubernetes
• Public Cloud
Required Qualifications
• 6+ years of experience in analytics domains, and deep understanding of ML operationalization and lifecycle management.
• 5+ years of deploying and monitoring analytical assets in batch/real-time business processes.
• 5+ years of SQL & Python programming experience leveraging strong software development principles.
• Experience in designing and developing AI applications and systems.
• Experience with real-time and streaming technology (i.e. Azure Event Hubs, Azure Functions, Pub/Sub, Kafka, Spark Streaming etc.)
• Experience with REST API/Microservice development using Python/Java.
• Experience with deployment/scaling of apps on containerized environment (AKS and/or GKE)
• Experience with Snowflake/BigQuery, Google Dataproc/Databricks or any big data frameworks on Spark
• Experience with RDBMS and NoSQL Databases and hands-on query tuning/optimization.