About the role
The Machine Learning Lead will oversee the full lifecycle of machine learning projects, from concept to production deployment. The role requires strong technical expertise and leadership to guide teams in delivering impactful AI-driven solutions.
Responsibilities:
- Design and implement scalable ML models for various business applications.
- Manage end-to-end ML pipelines, including data preprocessing, model training, and deployment.
- Fine-tune foundation models and create small language models for deployment on AWS Inferentia and Trainium.
- Deploy ML models into production environments using platforms such as AWS.
- Lead the implementation of custom model development, ML pipelines, fine-tuning, and performance monitoring.
- Collaborate with cross-functional teams to identify and prioritise AI/ML use cases.
Requirements:
- Expertise in ML frameworks like TensorFlow, PyTorch, and Scikit-learn.
- Strong experience deploying ML models on cloud platforms, especially AWS.
- In-depth knowledge of SageMaker and SageMaker Pipelines.
- Familiarity with RAG-based architectures, agentic AI solutions, Inferentia, and Trainium.
- Advanced programming skills in Python with experience in APIs and microservices.
- Exceptional problem-solving abilities and a passion for innovation.