What are the responsibilities and job description for the Senior ML Engineer position at Empower Professionals?
Job Details
Role: Senior ML Engineer
Duration: 12 Months Location: Atlanta GA (Fully onsite)
Job Description:
- ML Models Hosting Technical Architecture:
- Design and implement data storage solutions using ADLS and other relevant technologies.
- Develop and maintain model training environments using Databricks and other platforms.
- Optimize model run-time and latency for on-demand workloads.
- Create and manage API layers for model serving.
- Ensure robust security architecture for ML models and data.
- Creation, Training, and Serving of ML Models:
- Develop and deploy ML models using Databricks notebooks and GitLab for version control.
- Implement batch processing solutions using Databricks and other tools.
- Deploy models for real-time inference in on-demand environments.
- Collaborate with data engineers and data scientists to integrate models into production systems.
- Collaboration and Communication:
- Work closely with cross-functional teams to understand business requirements and translate them into technical solutions.
- Document processes, models, and code to ensure knowledge sharing and maintainability.
- Stay updated with the latest industry trends and technologies in machine learning and data engineering.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Proven experience in ML engineering, including model creation, training, and serving.
- Strong knowledge of Databricks, Unity Catalog, ADLS, and Databricks notebooks.
- Proficiency in using GitLab for version control.
- Experience with API development and deployment.
- Understanding of security best practices for ML models and data.
- Excellent problem-solving skills and attention to detail.
- Strong communication and collaboration skills.
Preferred Qualifications:
- Experience with cloud platforms such as Azure
- Familiarity with big data processing tools like Apache Spark.
- Knowledge of containerization and orchestration tools like Docker and Kubernetes.
- Platform management, SQL, cloud artificial intelligence, computer science, machine learning, science & research
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