Senior ML Ops Engineer - Dallas, TX

Oracle is a leading company that has powered many Digital Experiences for the Fortune 500 for the past 20 years. They are seeking a Senior ML Ops Engineer to design and implement scalable ML systems, overseeing the entire ML and MLOps lifecycle, while ensuring effective collaboration and communication within the team.

Responsibilities:

• Hands on with designing end to end scalable ML system (should have worked on recent projects within last 12 months)
• Hands on with implementation of scalable ML system. Proven ownership across entire or partial ML and MLOps lifecycle:
• Evaluation Techniques
• Machine Learning Algorithms
• Statistical Modeling
• End to end deployment
• Metric generation
• Model monitoring and deployment
• Prompt Engineering
• Hand on with ML Model optimization - quantization, pruning, or speculative decoding etc
• Hand on with ML Model system optimizations ie ability to quickly identify bottlenecks and resolve them from System perspective
• Programming & Frameworks: Hands on and have worked on recent projects (within last 12months) in:
• Java
• Library/dependency management
• Package and distribution management
• Algorithms
• Python
• Tensorflow / PyTorch
• Knowledge of DevOps principles and tools (e.g., CI/CD pipelines, Terraform)
• Strong understanding of containerization technologies (e.g., Docker, Kubernetes)
• Good Team worker & good collaborations skills
• Ability to abstract out details, define problem & have clear technical communication
• Ability to lead inter team communication
• Ability to write crisp and effective documentation
• Ensures that deadlines are met
• GCP ML Tech stack
• Experienced with Infrastructure as Code (IaC)
• Experience with big data technologies such as Apache Spark or Hadoop
• Stay informed about the ethical implications of machine learning eg: selection bias
• Model Training
• Data Analytics - figure out anomalies , skew , discrepancies
• Hands on with developing on device ML System
• Mentoring and Leadership
• Project Management

Requirements:

• Hands on with designing end to end scalable ML system (should have worked on recent projects within last 12 months)
• Hands on with implementation of scalable ML system. Proven ownership across entire or partial ML and MLOps lifecycle:
• Evaluation Techniques
• Machine Learning Algorithms
• Statistical Modeling
• End to end deployment
• Metric generation
• Model monitoring and deployment
• Prompt Engineering
• Hand on with ML Model optimization - quantization, pruning, or speculative decoding etc
• Hand on with ML Model system optimizations ie ability to quickly identify bottlenecks and resolve them from System perspective
• Programming & Frameworks: Hands on and have worked on recent projects (within last 12months) in:
• Java
• library/dependency management
• Package and distribution management
• Algorithms
• Python
• Tensorflow / PyTorch
• Knowledge of DevOps principles and tools (e.g., CI/CD pipelines, Terraform)
• Strong understanding of containerization technologies (e.g., Docker, Kubernetes)
• Good Team worker & good collaborations skills
• Ability to abstract out details, define problem & have clear technical communication
• Ability to lead inter team communication
• Ability to write crisp and effective documentation
• Ensures that deadlines are met
• GCP ML Tech stack
• Experienced with Infrastructure as Code (IaC)
• Experience with big data technologies such as Apache Spark or Hadoop
• Stay informed about the ethical implications of machine learning eg: selection bias
• Model Training
• Data Analytics - figure out anomalies , skew , discrepancies
• Hands on with developing on device ML System
• Mentoring and Leadership
• Project Management