Remote - Machine Learning Enginee with geo spatial and churn prediction And Azure

Overview

Remote
On Site
Hybrid
BASED ON EXPERIENCE
Contract - Independent
Contract - W2

Skills

Design Of Experiments
Generative Artificial Intelligence (AI)
Real-time
Scalability
Amazon Web Services
Google Cloud Platform
Google Cloud
Optimization
GitHub
Management
Regulatory Compliance
Privacy
Computer Science
Informatics
Predictive Modelling
Natural Language Processing
Microsoft Azure
Databricks
Python
SQL
R
Docker
Kubernetes
Artificial Intelligence
Continuous Integration
Continuous Delivery
DevOps
Collaboration
Telecommunications
Machine Learning (ML)
Cloud Computing

Job Details

Role: Machine Learning Engineer with Azure DataBricks
Location: Seattle, WA - Remote
Duration: Long term
Rates: DOE
Note: Core Machine Learning Engineers only (No GenAI and Data Scientist profiles)
Key Responsibilities
  • Model Engineering & Deployment: Build and maintain production-grade ML models with an emphasis on real-time inference, scalability, and reliability.
  • End-to-End ML Infrastructure: Design and implement scalable ML pipelines on AWS, Google Cloud Platform, or Azure.
  • Cross-Functional Collaboration: Partner with data scientists, data engineers, DevOps, and business stakeholders to continuously improve AI performance.
  • CI/CD Optimization: Develop and maintain CI/CD pipelines for ML using tools such as GitHub Actions.
  • Monitoring & Logging: Set up and manage tools to monitor system health and ML model performance.
  • Security & Compliance: Ensure ML systems meet telecom and other data privacy regulations.

Required:
  • 7+ years of experience as a Machine Learning Engineer.
  • Bachelor's degree in Computer Science, Artificial Intelligence, Informatics, or related field.
  • Strong knowledge of predictive modeling, NLP, and LLMs, including the use of the RAG framework.
  • Hands-on experience with Azure DataBricks.
  • basically more with geo spatial and churn prediction
  • Proficiency in Python, SQL, and either R or a comparable language.
  • Deep understanding of architecture, containerization (Docker, Kubernetes), and deployment strategies.
  • Proven experience building scalable, secure, and compliant AI/ML systems.
  • Expertise in CI/CD automation and DevOps collaboration.
Preferred:
  • Master's degree in a relevant field.
  • Experience working with Telecom systems.
  • Certifications in machine learning or cloud computing.
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