Job Location: Hyderabad/Secunderabad, Pune, Gurgaon/Gurugram, Chennai, Bangalore/Bengaluru
ML EngineerML Ops engineer :
Working will provide you an opportunity to work with industry leading client organizations, deep technology and domain experts, and global teams. University, our learning platform, provides ample learning opportunities starting with a structured onboarding program and carrying throughout various stages of your career. A variety of fun activities are also an integral part of our friendly work environment. Our flexible career paths allow you to grow into a program manager, a technical architect or a domain expert based on your skills and interests.
Role Description: ML EngineerML Operations Engineer
The ideal candidate is a hands-on technology developer with experience in developing scalable applications and platforms. They must be at ease working in an agile environment with little supervision. The person should be a self-motivated person with a passion for problem solving and continuous learning.
GCP Certified preferred.
Role and responsibilities :
– Project Management (50%)
– Front Door (Requirements, Metadata collection, classification & security clearance)
– Data pipeline template development
– Data pipeline Monitoring development & support (operations)
– Design, develop, deploy, and maintain production-grade scalable data transformation, machine learning and deep learning code, pipelines; manage data and model versioning, training, tuning, serving, experiment and evaluation tracking dashboards.
– Manage ETL and machine learning model lifecycle: develop, deploy, monitor, maintain, and update data and models in production.
– Build and maintain tools and infrastructure for data processing for AIML development initiatives.
Technical skills requirements :
The candidate must demonstrate proficiency in,
– Experience deploying machine learning models into production environment.
– Strong DevOps, Data Engineering and ML background with Cloud platforms
– Experience in containerization and orchestration (such as Docker, Kubernetes)
– Experience with ML trainingretraining, Model Registry, ML model performance measurement using ML Ops open source frameworks.
– Experience buildingoperating systems for data extraction, ingestion and processing of large data sets
– Experience with MLOps tools such as MLFlow and Kubeflow
– Experience in Python scripting
– Experience with CICD
– Fluency in Python data tools e.g. Pandas, Dask, or Pyspark
– Experience working on large scale, distributed systems
– PythonScala for data pipelines
– ScalaJavaPython for micro-services and APIs
– HDP, Oracle skills & Sql; Spark, Scala, Hive and Oozie DataOps (DevOps, CDC)
Nice-to-have skills :
– Jenkins, K8S
– Google Cloud certification
– Unix or Shell scripting
Qualifications :
– B.Tech., M.Tech. or MCA degree from a reputed university.
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