Machine Learning Operations

Key Skills

AWS, Azure, Bash scripting, Databricks, DevOps, Elasticsearch, Google Cloud Platform (GCP), Grafana, Kibana, Kubeflow, Kubernetes-K8s, Linux Operating system, MLflow, Python Programming, Shell Scripting language, Splunk

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.