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• No of position :- ( 1 )

• Post :- 30th Mar 2022

ML Engineer - Azure

5+ years of software development experience in below skills.

Competent ML engineer, who is independent, results driven and is capable of taking business requirements and building out the technologies to take it to production.

Big Data ML Engineer with expert level experience in Hadoop ecosystem and real-time analytics tools including PySpark/Scala-Spark/Hive/Hadoop CLI/MapReduce/ Storm/Kafka/Lambda Architecture/Mongo.

Familiar with job scheduling challenges in Hadoop.

Experienced in creating and submitting Spark jobs.

Experience in high performance tuning and scalability.

Experience in working on real time stream processing technologies like Spark structured streaming, Kafka, FLink + rocksDB.

Expertise in Python/Spark and their related libraries and frameworks.

Experienced with Spring Framework and Spring Boot.

Experience in building training Machine Learning (ML) pipeline and efforts involved in ML Model deployment.

Experience in other ML concepts – Real time distributed model inferencing pipeline, Champion/Challenger framework, A/B Testing, Model performance Scorecard and assessment, Retraining framework, etc.

Unix/Linux expertise; comfortable with Linux operating system and Shell Scripting.

Working Experience in Redis DB – Writing to and from Redis.

Experience in Azure cache.

Experience in creating Kubernetes open-source container-orchestration system for automating application deployment, scaling, and management; (Experience in Azure is preferable).

Experience in tweaking/using Jenkins, deployment orchestration and/or Kubernetes for CI/CD pipeline.

Creating/modifying Dockers, microservices and deploying them via Kubernetes.

PL/SQL, RDBMS background with Oracle/MySQL.

Design, Development, Unit and Integration testing of complex data pipelines and to handle data volumes to derive insights.

Ability to optimize code to be able to run efficiently with stipulated SLA.

Excellent problem-solving skills, with attention to detail, focus on quality and timely delivery of assigned tasks.