Role: Machine Learning Engineer with Azure DataBricks Location: Seattle, WA - Remote Duration: Long termRates: 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, GCP, 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.
