Machine Learning Infrastructure Specialist
2 days ago
Job Title: Machine Learning Infrastructure Specialist
We are seeking an experienced Machine Learning Infrastructure Specialist with strong expertise in Large Language Model (LLM) deployment, optimization, and scalable model serving.
Main Responsibilities:
- Design, deploy, and manage LLMs in production environments utilizing industry-standard tools.
- Build and optimize scalable ML pipelines for training, fine-tuning, and inference to ensure efficient model performance.
- Implement best practices for MLOps including continuous integration and delivery for ML, experiment tracking, and automated retraining workflows to streamline operations.
- Optimize model performance through quantization, pruning, distillation, and GPU/TPU acceleration techniques.
- Manage and monitor LLM serving infrastructure with containerization and orchestration tools to guarantee reliability, scalability, and security.
- Collaborate with data scientists and researchers to integrate models into real-world applications, ensuring seamless communication and successful implementation.
- Guarantee the observability and monitoring of deployed ML systems to detect drift and optimize resource utilization.
Required Skills & Qualifications:
- 4-6 years of hands-on experience in MLOps / ML Engineering with a focus on large-scale deployments.
- Expertise in LLM deployment, fine-tuning, and inference optimization, including knowledge of popular frameworks.
- Strong understanding of Kubernetes, Docker, and cloud platforms (AWS/GCP/Azure) for efficient model serving.
- Experience with model compression, distributed training, and serving frameworks to ensure optimal performance.
- Proficiency in Python, PyTorch/TensorFlow, and relevant tooling for efficient development and deployment.
- Familiarity with CI/CD pipelines and version control systems for smooth collaboration and integration.
- Understanding of observability and monitoring tools for effective system management.
PREFERRED SKILLS:
- Knowledge of LangChain, LlamaIndex, or similar frameworks for enhanced LLM capabilities.
- Exposure to multi-modal LLMs and real-time inference systems for improved performance.
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