MLOps Engineer: Scale AI Pipelines & Platforms

Kensho Technologies

New York (NY)

On-site

USD 120,000 - 150,000

Full time

14 days+

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Benefits offered by this job

Medical, Dental, and Vision insurance
Unlimited Paid Time Off
26 weeks paid Parental Leave
401(k) plan with employer matching
Tuition assistance for degree programs
Snacks and drinks provided
Dog-friendly office
Bike sharing program memberships
Mentoring and learning opportunities
Networking opportunities at conferences

Job summary

An innovative tech company in New York is seeking a member for their MLOps team. This role involves developing tools and services to enhance the ML workflow, while closely collaborating with ML engineers to identify challenges and provide effective solutions. The ideal candidate will have over 2 years of experience in ML infrastructure, strong Kubernetes and Python skills, as well as a good understanding of cloud platforms like AWS. They will also help train teams on best practices and promote efficient processes in ML product development.

Qualifications

  • 2+ years of experience in ML infra, ML Ops, or similar.
  • Experience with distributed systems and cloud platform understanding.
  • Familiarity with ML concepts and software engineering best practices.

Responsibilities

  • Develop tools and services for the ML workflow.
  • Work with ML engineers to identify pain points and solutions.
  • Provide training for ML teams on best practices.

Skills

Kubernetes management
Python proficiency
Communication skills
Understanding of ML concepts
Cloud services (AWS)

Education

Experience in ML infra or similar

Tools

Ray
Amazon EKS
Airflow
Terraform
Git

Job description

An innovative tech company in New York is seeking a member for their MLOps team. This role involves developing tools and services to enhance the ML workflow, while closely collaborating with ML engineers to identify challenges and provide effective solutions. The ideal candidate will have over 2 years of experience in ML infrastructure, strong Kubernetes and Python skills, as well as a good understanding of cloud platforms like AWS. They will also help train teams on best practices and promote efficient processes in ML product development.
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