Job
Description
Job Description : As an LLMOps Engineer, you will play a crucial role in the deployment, maintenance, and optimization of large language models (LLMs). Your responsibilities will span the entire lifecycle of LLMs, from initial deployment to ongoing operations, ensuring optimal performance, scalability, and reliability. Key Responsibilities : LLM Deployment and Integration - Deploy and integrate large language models into production environments, ensuring seamless integration with existing systems and applications. Infrastructure Planning and Scaling - Collaborate with cross-functional teams to plan and design the infrastructure required for LLM deployment. Implement scalable solutions to accommodate growing data volumes and user loads. Automation of Deployment Processes - Develop and maintain automation scripts and tools for efficient deployment, scaling, and versioning of LLMs. Streamline deployment processes to minimize downtime. Continuous Monitoring and Alerting - Implement monitoring systems to track LLM performance metrics. Set up alerts for potential issues and respond promptly to ensure uninterrupted service. Performance Monitoring and Optimization - Monitor the performance of LLMs in real-time, conduct regular assessments, and implement optimizations to enhance efficiency and responsiveness. Fault Tolerance and Disaster Recovery - Design and implement fault-tolerant systems for LLMs, incorporating strategies such as redundancy, sharding, and replication. Develop and maintain disaster recovery plans. Security Measures Implementation - Implement robust security measures to safeguard LLMs and associated data. Ensure compliance with data security regulations and industry standards. Collaboration with NLP Engineers and Data Scientists - Collaborate with NLP (Natural Language Processing) engineers and data scientists to understand model requirements and implement necessary infrastructure adjustments. Skills & Tools Infrastructure as Code (IaC) - Experience with IaC tools such as Terraform or Ansible for automating infrastructure provisioning. Containerization and Orchestration - Proficiency in containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) for managing LLM deployments. Cloud Platforms - Familiarity with cloud platforms such as AWS (Bedrock), Azure, or GCP, and experience in deploying and managing applications in a cloud environment. Monitoring and Logging Tools - Knowledge of monitoring tools (e.g., Prometheus, Grafana) and logging systems (e.g., ELK stack) for real-time performance monitoring and analysis. Security Measures - Understanding of security guardrails using tools like LLM Guard and familiarity of how to mask / redact / obfuscate sensitive data, protect the input and output of toxic and harmful content to / from LLMs and understand the performance implications of the same. Scripting and Automation - Proficient in scripting languages such as Python, Shell, or similar, and experience in automating deployment and maintenance processes.
