Job Description:

 

Core Responsibilities:

  • Strong understanding of Linux administration.
  • Strong understanding of AWS/GCP/Azure cloud.
  • In depth understanding of networking.
  • Good in python.
  • Strong understanding of machine learning lifecycle.
  • Design and automate a process for mlops implementation.
  • Implement data versioning, model versioning, code versioning.
  • Model deployment at scale, monitoring and alerting with drift management.
  • Create model retraining pipeline.
  • Evaluate and choose technology stacks that best fit client data strategy and constraints.
  • Write/rewrite the code to scale model training/deployment.

Qualification:

3+ years’ experience in Software Engineering and DevOps, 2+ years’ experience in machine learning development and deployment.

 

Technical Skills:

  • Need to be strong in Python and Bash/Shell Scripting. (Must)
  • Understanding of machine learning model development. (Must)
  • Mlflow, DVC, Databricks, Kubeflow, Distributed model training(RAY), Grafana
  • Hands on with Data versioning and monitoring tools.
  • Machine learning experiment tracking.
  • Known with distributed model training techniques.
  • (Must)
  • Experience on Azure/GCP (Google Cloud Platform)/AWS (Must)
  • Experience in Linux. (Must)
  • Experience in Ansible/Chef/Puppet. (Must)
  • Log Management Tools like ELK (Elastic Search, Logstash, Kibana), Splunk. - Added Advantage.
  • Knowledge about big data system such as (InfluxDB or ElasticSearch or Cassandra) - Added Advantage.
  • In depth knowledge about Networking, UNIX and low level OS internals. - Added Advantage.

 

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