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We are looking for a Machine Learning Engineer to drive the development of a self-serve AI platform and enhance systems for training, optimizing, and maintaining large language models. The ideal candidate will combine deep technical expertise in model training/fine-tuning and optimization with strong system architecture skills to ensure robust performance, scalability, and regulatory compliance.
Must Haves:
- Proven experience in training and fine-tuning machine learning models, a huge plus for experience specifically with large language models
- Familiarity with model performance optimization techniques (quantization, distillation, etc.) and associated tooling.
- Hands-on experience with AWS SageMaker, Google Vertex AI and/or other services essential for machine learning deployments.
- Strong experience in training data preparation, storage, versioning, and quality assurance.
- Expertise in setting up monitoring systems for model evaluation, drift detection, and compliance
Nice to Have:
- AWS Bedrock Google Vertex AI, Azure ML or similar managed ML platforms
- Terraform and Infrastructure-as-a-Code experience
Key Responsibilities:
- Train and fine-tune machine learning models, with a focus on large language models (LLMs).
- Develop, refine, and implement performance optimization techniques such as quantization, distillation, and cost optimization techniques
- Build infrastructure and tooling for LLM continuous fine-tuning
- Create automated pipelines for training data preparation, storage, and version management
- Develop and maintain company-hosted LLM deployment infrastructure using AWS SageMaker, Google Vertex AI or Azure ML and similar services
- Work closely with data scientists, DevOps engineers, and stakeholders to define technical requirements and iterate on solution designs
- Contribute to the development of end-to-end test automation frameworks for functional and performance evaluation
