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AI/ML Consultant with MLOps, Python, Azure, Kubernetes, Spark, and ETL expertise

AI/ML Consultant with MLOps, Python, Azure, Kubernetes, Spark, and ETL expertise
Amaze Systems
7 months 1 week ago

Job Opportunity Summary

Hi, we currently have a few job openings that may interest you. Please find below a summary of one of our current opportunities.

Kindly let me know your interest and share your available updated resume/details ASAP.

Job Details

  • Role: Consultant AI/ML, MLOps, Python, Azure, Kubernetes, Spark, ETL
  • Location: CA - Hybrid Role, No Remote - San Francisco or Cupertino Office in Hybrid Model 3 days per week
  • Duration: 6 Months+

Job Description:

  • MLOps / Client Engineering => 8/10 => 6 to 8 years of experience
  • Platform Development / MicroServices / Arch => 7/10 => 8 to 10 years of experience
  • Docker/Containers/Kubernetes => 6/10 => 5 to 6 years of experience
  • Data Science / Machine Learning => 5/10 => 5 to 6 years of experience
  • Azure Highly preferred to have the experience => 6 to 8 years of experience
  • Python must have => 8 to 10 years of experience
  • Spark- Required => 4 to 5 years of experience
  • Client tools experience such as AzureML/MLFlow/Databricks/Kubeflow etc. - Deployed & worked on some of these tools

Responsibilities

Build, modernize and maintain the Client's AI/Client Platform & related frameworks/solutions.

Participate and contribute to architecture & design reviews.

Build/Deploy AI/Client platform in Azure with open-source applications (Argo, Seldon) and/or cloud/SaaS solutions (Azure Client, Databricks, Truera).

Machine Learning Pipelines and Data Processing

You will design, develop, test, deploy, and maintain distributed & GPU-enabled Machine Learning Pipelines using K8s/AKS based Argo Workflow Orchestration solutions.

Collaborate with Data Scientists.

Enable/Support platform to do distributed data processing using Apache Spark and other distributed/scale technologies.

APIs and Applications Deployment

Build ETL pipelines, ingress/egress methodologies in context to AIML use-cases.

Build highly scalable backend REST APIs for metadata management and other business needs.

Deploy Application in Azure Kubernetes Service using GitLab CICD, Jenkins, Docker, Kubectl, Helm, Terraform, and Manifest.

Code Review and Performance Testing

Review code developed by other developers to ensure best practices.

Work with relevant engineering, operations, business lines, and infrastructure groups to ensure effective architectures and designs.

Perform functional, benchmark & performance testing and tuning to achieve performant AIML workflows.

Skills and Technologies Required

  • Client Platform / Client Engineering => 8 out of 10 years of experience
  • Platform Development / MicroServices / Arch => 7/10 years of experience
  • Docker/Containers/Kubernetes => 6/10 years of experience
  • Data Science / Machine Learning => 5/10 years of experience
  • Azure Highly preferred to have the experience
  • Python must have
  • Experience in software development and with data structures/algorithms (6-8 years)
  • Good understanding of distributed systems like Spark and Kafka - Good to Have => 5/10
  • Good understanding of security - TLS and RBAC
  • Client tools experience such as Argoworkflow/ AzureML/MLFlow/Databricks/etc.
  • AI tools experience such as Generative AI, ChatGPT, MetaGPT, LLM, Llama 2 etc.

Ashish

Amaze Systems

Expertise level

Work arrangement

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