LLM Infrastructure Engineer

About the Role

As an LLM Infrastructure Engineer at Hire Hangar, you will play a crucial role in shaping the future of machine learning by building and maintaining the systems that support large language models. This position is designed for individuals who thrive in a dynamic, fully remote environment and are eager to collaborate with talented teams across various global locations. In your first few months, you will engage with machine learning engineers to understand their needs, optimize infrastructure, and ensure seamless deployment of models into production. Your work will directly impact the efficiency and reliability of advanced AI systems, making it an exciting opportunity for those passionate about technology and innovation.

Key Responsibilities

• You will design, build, and maintain the infrastructure necessary for training, fine-tuning, and serving large language models, ensuring high performance and reliability.
• Your role will involve deploying and scaling LLM inference services, focusing on minimizing latency and maximizing throughput for optimal performance.
• You will manage GPU compute resources effectively, optimizing workload scheduling to ensure efficient utilization and performance.
• Building and maintaining data pipelines that support model training and evaluation will be a key part of your responsibilities.
• Implementing monitoring, logging, and alerting systems to track model performance and system health will be essential to your role.
• You will work to optimize infrastructure costs while maintaining high availability and performance standards.
• Automating deployment workflows using infrastructure-as-code and CI/CD practices will be a crucial aspect of your daily tasks.
• Collaboration with machine learning engineers and researchers is vital to transition models from experimentation to production effectively.
• You will proactively identify bottlenecks within the system, investigate root causes, and resolve infrastructure-related issues to maintain operational efficiency.

Requirements & Qualifications

• A proven background in building large language model infrastructure is a must for this role, showcasing your technical expertise.
• Hands-on experience with deploying and serving machine learning models in production environments is essential.
• Strong programming skills in Python, particularly in systems or backend development, are required to excel in this position.
• You should have experience with containerization and orchestration tools such as Docker and Kubernetes, essential for modern deployment practices.
• A solid understanding of distributed systems, GPU computing, and performance optimization will significantly benefit your work.
• Excellent written and spoken English communication skills are necessary for effective collaboration in a remote environment.
• Prior remote work experience is required, along with familiarity with collaboration tools like Slack, Zoom, and Google Workspace.

What You’ll Gain

• Enhance your technical skills in machine learning infrastructure and gain hands-on experience with cutting-edge technologies.
• Build a strong professional network by collaborating with experts in the field of AI and machine learning.
• You will have opportunities for career advancement as you contribute to significant projects and initiatives within the company.
• Working in a remote setting allows for flexibility and the chance to balance your professional and personal life effectively.
• Gain insights into the operational challenges and solutions in large-scale AI deployments, enhancing your problem-solving capabilities.

How to Apply

If you are ready to take on this exciting challenge and be part of a pioneering team, please submit your application. You can apply through this link: Apply here.