Devops Architect

  • Alameda, CA
  • Posted 19 hours ago | Updated 19 hours ago

Overview

On Site
$150,000 - $180,000
Full Time

Skills

Amazon EC2
Amazon S3
Amazon Web Services
Continuous Integration
Data Analysis
Data Migration
Design Of Experiments
Evaluation
GitHub
Machine Learning Operations (ML Ops)
Rapid Prototyping
Machine Learning (ML)
Storage
SAP R/3
Regulatory Compliance

Job Details

We are in Critical need of Technical Architect for Fulltime Hire in Alameda, CA

Role : Technical Architect

Location : Alameda, CA

Duration : Long Term

Job Description :

Primary Responsibilities :
We need a strong Devops architect with understanding of MLOps capabilities having experience in supporting and providing Infrastructure solutions to a Data Analytics platform.
1. Day to day hands on experience in the following AWS services
a. Compute:
i. EKS: Provide Operational / Solutioning on K8s
ii. Lambda with AWS Event Bridge
b. AWS DB and Storage services:
i. S3 in use of medallion architecture
ii. FSx Lustre , ONTAP, Windows
iii. RDS
c. AWS Data / Reporting services:
i. Airflow MWAA
ii. AWS Glue catalog, Glue crawlers
iii. Redshift
iv. Tableau
d. Data migration services
i. S3 Replicaton across accounts in Control tower setup
ii. Data Synch from on prem to S3
iii. AWS Transfer Family
In addition to the above the expectation is to know the AWS foundational services such as VPCs, ASG, EC2, Secrets Manager, KMS,
2. Strong programming skills in Python and/or R
3. Solid understanding of CI/CD principles and experience with tools like GitHub Actions
4. Design and build scalable MLOps pipelines on AWS, automating model training, evaluation, deployment, and monitoring
5. Manage version control for models, data, and configurations, ensuring reproducibility and compliance Secondary Responsibilities:
1. Automate retraining and rollback processes to maintain model accuracy and reliability over time
2. Document ML systems, workflows, and infrastructure for knowledge sharing and compliance
3. Support research and experimental design by enabling rapid prototyping and deployment of ML solutions
4. Collaborate with data scientists, software engineers, and DevOps teams to integrate ML models into operational workflows and applications

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