Design & Implement ML Workflows

Build and Scale Our MLOps Platform

• Design, develop, and optimise a high-throughput model inference system for production-grade AI.
• Create streamlined workflow pipelines for model training, testing, monitoring, and deployment, leveraging cloud-native tools.

Cloud & Container Management

• Deploy and manage applications using AWS services, Kubernetes, Docker, and other DevOps best practices.

LLM Deployment & Fine Tuning

• Drive the deployment and fine-tuning of large language models (LLMs) while ensuring efficient training pipelines and model hosting.

Monitoring & Performance Optimization

• Implement monitoring (using Prometheus/Grafana and similar tools) and logging solutions to ensure system reliability and to optimise model throughput.

Collaborate Across Teams

• Work closely with Machine Learning engineers to enable their delivery.

Cloud & DevOps Expertise

• Experience with AWS, Kubernetes, and Docker.
• Proven skills in designing and managing CI/CD pipelines.

MLOps & LLM Operations

• Hands-on experience with training pipelines, model hosting, and throughput optimisation.
• Expertise in deploying and fine-tuning large language models.

Monitoring & Performance

• Proficiency with monitoring tools such as Prometheus and Grafana.

Programming & Automation

• Strong proficiency in Python, with experience in developing production applications.

Data Engineering & Streaming

• Familiarity with data streaming tools and Elastic to ensure high performance in data-driven environments.