Resource Informatics Group
Machine Learning Engineer with Azure Data Bricks
Resource Informatics Group, Seattle, Washington, us, 98127
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
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. 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. Preferred:
Master's degree in a relevant field. Experience working with Telecom systems. Certifications in machine learning or cloud computing.
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. 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. Preferred:
Master's degree in a relevant field. Experience working with Telecom systems. Certifications in machine learning or cloud computing.