Data Science Engineer

Role:

Required Technical Skill Set:

Desired Experience Range:

Location of Requirement:

Desired Competencies (Technical/Behavioral Competency)

Must-Have

· 4+ years of experience in Data Science, ML & DL.

· Well versed with building Algorithms to predict Network Performance degradation.

· Expertise in Acumos AI and MLFlow which are platforms to manage and package ML lifecycle.

· Microservices, Cloud native technologies, Serverless Computing

· Cloud computing, SDN, NFV

· Docker, Kubernetes, Redhat Openshift, Kubeflow

· Strong development skills including Linux, Shell, Python, Java, Scala

Good-to-Have

· Exposure to GCP, AWS and Azure services.

· Exposure to Cloud Native Tools: Docker,Kubernetes, Jaeger, Fluentd, Elastic Search, Kibana, Istio, Harbor, Prometheus, Grafana, Ansible, Git, Jenkins, Gerrit, Knative

SN Role descriptions / Expectations from the Role

1. Data Engineer. Hands-on software development – Strong hands on experience in designing and developing mission critical, highly complex applications

· Development of Multi-Processor Performance Benchmarking (MPPB),a solution to optimize performance of various workloads (AI, NFV, Database, and Webservers) by tuning the processor configurations.

· Development of Microservices Development and Deployment Platform(MDDP) a platform that aids in the development and deployment of microservices with Kubernetes, OpenShift as underlying orchestrators.

· Development of Ascent, a framework for upgrading microservice based applications in targeted regions using Istio and Knative.

· Develop machine learning model to predict the Network element (Switches or Routers) failure in near future from syslogs and network performance metrics data.

· Develop machine learning model for a client which needs to predict the downlink throughput in telecom. using the TensorFlow package using python.

· Develop an algorithm for radio network optimization, which suggests the radio network engineer to change the configuration of the base station. Based on learned patterns from the several base stations in the telecommunications.

· Explored around Acumos AI and MLFlow which are platforms to manage and package ML lifecycle.