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Machine Learning Engineer

RIVAGO INFOTECH INC.
Scottsdale, AZ, United States
2 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source
dice.com

Tech stack

Airflow Amazon Web Services Computer Programming Continuous Integration Extract Transform Load (ETL) Distributed Systems Github Python (Programming Language) Machine Learning Performance Tuning Tensorflow Azure Machine Learning
+9 more
Data Ingestion Pytorch Gitlab-ci Scikit Learn Kubernetes Machine Learning Operations Software Version Control Data Pipelines Jenkins

Job description

  • Design and implement end-to-end ML pipelines from data ingestion to model deployment
  • Build and manage CI/CD pipelines for ML models (training, testing, deployment)
  • Automate model monitoring, retraining, and performance optimization
  • Collaborate with Data Scientists and Data Engineers for productionizing ML models
  • Ensure scalability, reliability, and security of ML systems
  • Manage model versioning, experiment tracking, and lifecycle management
  • Implement best practices for governance, compliance, and reproducibility

Requirements

  • Strong programming skills in Python
  • Experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn
  • Hands-on experience with MLOps tools: MLflow, Kubeflow, Airflow, SageMaker, Azure ML
  • Knowledge of CI/CD tools: Jenkins, GitHub Actions, GitLab CI
  • Experience with cloud platforms: AWS
  • Strong understanding of data pipelines, ETL processes, and distributed systems

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on dice.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

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6:36 min

Funding open source through GitHub Accelerator and Sponsors

Stormy Peters · WWC 2023

1:02 min

Applying an ETL methodology to infrastructure configuration management

Axel Barbier · WWC 2023

2:35 min

Preventing remote code execution in PyTorch models

Balázs Kiss · WWC 2023

5:28 min

Navigating machine learning operations and maturity level frameworks

Julian Joseph · LIVE

2:40 min

Using GitHub primitives for internal documentation and corporate operations

Kyle Daigle · Coffee With Developers

2:56 min

Leveraging domain-specific frameworks and RAPIDS for data science

Paul Graham Paul Graham · WWC 2025

Videos

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25:48
WWC 2024 • almost 2 years ago

The Road to MLOps: How Verivox Transitioned to AWS

Elisabeth Günther

Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding) thumbnail
29:32
WWC Europe 2026 • about 1 month ago

Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)

Christoph Lohrmann, Utz Bacher

Innovating Developer Tools with AI: Insights from GitHub Next thumbnail
42:38
LIVE • over 1 year ago

Innovating Developer Tools with AI: Insights from GitHub Next

Krzystof Czieslak

DevOps for Machine Learning thumbnail
44:40
WWC 2021 • about 5 years ago

DevOps for Machine Learning

Hauke Brammer

Serverless deployment of (large) NLP models thumbnail
39:02
LIVE • about 5 years ago

Serverless deployment of (large) NLP models

Marek Suppa

Our GitOps approach for deploying an Identity Provider and an API Gateway in a SaaS company thumbnail
17:31
WWC 2023 • almost 3 years ago

Our GitOps approach for deploying an Identity Provider and an API Gateway in a SaaS company

Axel Barbier

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