Overview
On Site
BASED ON EXPERIENCE
Contract - W2
Contract - Independent
Skills
Design Of Experiments
Real-time
Scalability
Amazon Web Services
Google Cloud Platform
Google Cloud
Innovation
Generative Artificial Intelligence (AI)
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 Data Bricks
Location: Seattle, WA
Duration: Long term
Rates: DOE
Note: Core Machine Learning Engineers only (No GenAI and Data Scientist profiles)
Key Responsibilities
Required:
Location: Seattle, WA
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.
- AI/GenAI Strategy: Lead development of advanced LLM and RAG frameworks; drive innovation in ML/GenAI integration strategies.
- 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:
- 5+ 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.
- 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.
- Master's degree in a relevant field.
- Experience working with Telecom systems.
- Certifications in machine learning or cloud computing.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.