Diverse Lynx | MLOps Engineer | 2023-03

Summarization

1. Key Words: ML Pipeline Development, ML Serving, Infra Automation
2. Programming Languages and Frameworks: Python, PyTorch, TensorFlow, Keras
3. Technical Areas: MLOps, Machine Learning, Data Engineering, DevOps
4. Cloud and DevOps: GCP, Kubernetes, GKE, GCP Dataflow, Dataproc, Vertex AI, Terraform
5. Technical Achievements: Built end-to-end systems, Deployed APIs in production, Set up model monitoring and autoscaling
6. Technical Skills: CI/CD, Container Development, Data Processing, Software Testing, API Integration
7. Soft Skills: Communication, Translating Business Needs, Stakeholder Engagement
8. Responsibilities: Develop ML systems, Design data pipelines, Convert offline models, Evaluate technologies, Facilitate ML deployment

1. Requirements

2. Responsibilities

Geomagical Labs | Realtime MLOps Engineer | 2023-03

Summarization

1. Key Words: ML Pipeline Development, Monitoring and Observability, System Integration
2. Programming Languages and Frameworks: Python
3. Technical Areas: Computer Vision, Data Processing, Pipeline Orchestration, 3D Graphics
4. Cloud and DevOps: Kubernetes, Docker
5. Technical Achievements: Improved computer vision pipelines, Developed global consumer products
6. Technical Skills: DAG Pipeline Orchestration, Git, JIRA, Queue/Log Databases (RabbitMQ, Kafka)
7. Soft Skills: English Communication, Team Coordination, Sprint Planning
8. Responsibilities: Architect low-latency pipelines, Enhance pipeline monitoring, Collaborate with research teams, Grow architecture to support initiatives

1. Desc

2. Requirements

3. Preferred Qualification

4. Responsibilities

Apple | ML Infrastructure Engineer | 2023-03

Summarization

1. Key Words: ML Development, ML Pipeline Development, Performance Optimization, Technology Adoption
2. Programming Languages and Frameworks: Python, PyTorch, TensorFlow
3. Technical Areas: Machine Learning, Autonomous Systems, Robotics, High-Performance Computing
4. Cloud and DevOps: Distributed Cloud GPU Training, ML Model Serving (TorchServe, TensorFlow Serving, NVIDIA Triton), Cloud Data Processing (Spark, Dask, ElasticSearch, Presto, SQL)
5. Technical Achievements: Developed scalable ML approaches, Built end-to-end data systems, Improved distributed cloud GPU training
6. Technical Skills: Debugging, Critical Thinking, Software Engineering, MLOps Support
7. Soft Skills: Analytical Problem Solving, Teamwork, Fast-Paced Environment Adaptability, Mentoring
8. Responsibilities: Build distributed ML systems, Architect MLOps platforms, Enhance ML system tools, Collaborate on data problems, Mentor engineers

1. Desc

2. Requirements

3. Responsibilities

콘텐츠웨이브(주) | MLOps 개발자 | 2023-03

Summarization

1. Key Words: ML Development, ML Pipeline Development, Performance Optimization
2. Programming Languages and Frameworks: Python, Java, Scala, Scikit-learn, TensorFlow, PyTorch
3. Technical Areas: Machine Learning, Deep Learning, Recommendation Systems, Campaign Targeting
4. Cloud and DevOps: GCP, GPU/TPU environments, CI/CD/CT for ML pipelines
5. Technical Achievements: Developed ML services on GCP, Built ML pipelines, Experience with distributed processing of large datasets
6. Technical Skills: Data Application Development, Distributed Data Processing, Big Data Platforms
7. Responsibilities: Develop ML services using Wavve data, Conduct Python projects with ML libraries, Design and build ML pipelines

1. Requirements

2. Responsibilities

3. Tools