AI Platform Engineer(MLOps)

As an AI Platform Engineer, you should have a minimum of 8 years of experience in the relevant field, with internships, prototypes, and personal projects not being considered. Proficiency in coding is essential, preferably in Python or Java. You should possess experience in DevOps Culture, including owning development and deployment processes end-to-end. Additionally, you should have hands-on experience in building or deploying solutions in the cloud, either using Cloud Native services like S3, Lambda, AWS Batch, and ECS, or Cloud Agnostic solutions such as Kubernetes services. Having experience in Continuous Integration and Continuous Deployment (CICD) is crucial, with familiarity in tools like Github Actions or Jenkins. Proficiency in Infrastructure as Code using Terraform is highly desirable. Moreover, you should have expertise in at least one of the following focus areas: 1. Big Data: Experience in building Big Data pipelines or platforms to process petabytes of data using technologies like Kafka, Debezium, Lakehouse (Hudi/Iceberg/Delta Lake), Spark, Glue, and EMR is preferred. 2. Graph Databases: Proficiency in ingesting and consuming data in Graph Databases like Neo4J, JanusGraph, or DGraph would be beneficial. 3. MLOps: Knowledge in infrastructure and operation, including technologies such as Kafka, KNative, KServe, Nvidia Triton, Ray Serve, MLflow, and Sagemaker, is advantageous. Your role as an AI Platform Engineer will involve leveraging your expertise in these areas to contribute to the development and deployment of cutting-edge AI solutions within a dynamic and innovative environment.,