GlobalLogicRemote
Senior ML Engineer
United States of America
Full-time
3 weeks ago
$140,000 - $150,000 USD
H1B Sponsor Likely
Key skills
ML system designML model optimizationPythonTensorFlowDevOps principlesContainerization technologiesGCP ML Tech stackBig data technologiesData AnalyticsModel TrainingJavaMachine LearningMLPyTorchMLOpsAnalyticsSparkApache SparkHadoopGCPTerraformKubernetesDockerCI/CD
About this role
GlobalLogic is a leader in digital engineering, helping brands design and build innovative products and platforms. The Senior ML Engineer will be responsible for designing and implementing scalable ML systems, optimizing models, and ensuring efficient deployment and monitoring of ML solutions.
Responsibilities:
- Hands on with designing end to end scalable ML system
- 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 12 months) in: Java (Library/Dependency Management, Algorithms), Package and distribution management, Python, TensorFlow / PyTorch
- Knowledge of DevOps principles and tools (e.g., CI/CD pipelines, Terraform)
- Strong understanding of containerization technologies (e.g., Docker, Kubernetes)
- 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
Requirements:
- Hands on with designing end to end scalable ML system
- 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 12 months) in: Java (Library/Dependency Management, Algorithms), Package and distribution management, Python, TensorFlow / PyTorch
- Knowledge of DevOps principles and tools (e.g., CI/CD pipelines, Terraform)
- Strong understanding of containerization technologies (e.g., Docker, Kubernetes)
- Bachelor's or Master's degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field
- 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