MLOps/LLMOps Engineer

Key Responsibilities:

• Design and implement LLM-specific deployment architectures with Docker containers for both batch and real-time inference


• Configure GPU infrastructure on-premises or in the cloud with appropriate CI/CD pipelines for model updates


• Build comprehensive monitoring and observability systems with appropriate logging, metrics, and alerts


• Implement load balancing and scaling solutions for LLM inference, including model sharding if necessary


• Create automated workflows for model retraining, versioning, and deployment


• Optimize infrastructure costs through intelligent resource allocation, spot instances, and efficient compute strategies


• Collaborate with the Cyber team on implementing appropriate security controls for GenAI applications


• Develop automated testing frameworks to ensure consistent output quality across model updates

Expected Skillset:

• DevOps + ML: Expertise in Kubernetes, Docker, CI/CD tools, and MLflow or similar platforms


• Cloud & Infrastructure: Understanding of GPU instance options, cloud services (AWS/Azure/Google Cloud Platform), and optimization techniques


• Automation: Proficiency in Python, Bash, and infrastructure-as-code tools like Terraform or Ansible


• LLM-Specific Frameworks: Experience with tools like TensorBoard, MLFLow, or equivalent for scaling LLMs


• Performance Optimization: Knowledge of techniques to monitor and improve inference speed, throughput, and cost


• Collaboration: Ability to work effectively across technical teams while adhering to enterprise architecture standards

Oscar Associates Limited (US) is acting as an Employment Business in relation to this vacancy.