Design, build, and maintain scalable MLOps pipelines (CI/CD) for training, fine-tuning, evaluating, deploying, and monitoring Generative AI, LLM, NLP, and RAG models
Manage and optimise infrastructure (cloud-based, potentially hybrid) required for hosting large models, including GPU resource management and specialised serving frameworks
Implement and manage deployment strategies suitable for large and complex AI models
Operationalise and manage components specific to RAG systems, such as vector databases, embedding pipelines, and knowledge base update workflows
Automate workflows for data ingestion, embedding generation, and indexing required for RAG and other NLP tasks
Implement robust practices for model versioning, prompt versioning, experiment tracking, artifact management, and reproducibility within the Gen AI context
Job Offer
The opportunity to play a pivotal role in shaping an AI-forward, data-driven culture in a dynamic and data-ready work environment
Requirements
Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field
3–5+ years as an MLOps Engineer with a strong focus on operationalising machine learning models
Demonstrable experience in designing, building, and maintaining CI/CD pipelines for ML models
Strong programming skills, particularly in Python
Familiarity with ML libraries/frameworks such as PyTorch, TensorFlow, etc.
