On a daily basis, you will
- Collaborate with AI researchers and engineers to bridge the gap between research and production.
- Deploy, manage, and monitor LLM/ML models in both cloud and on-premise environments, ensuring smooth integration into our research and production pipelines.
- Support engineers in integrating ML models into production, ensuring a smooth handoff from research to product teams.
- Automate ML workflows with CI/CD pipelines for model deployment and continuous integration.
- Design and maintain flexible ML workflows to support rapid experimentation.
- Enable fast iteration by setting up tools for model tracking, logging, and comparison (e.g., MLflow, DVC, Weights & Biases).
- Manage research-friendly cloud environments that allow easy deployment and experimentation.
- Optimize model inference for speed, efficiency, and scalability while balancing research flexibility.
- Ensure AI models and experiments are reproducible by structuring model storage, versioning, and benchmarking practices.
The skills you will demonstrate
- Academic background with a university degree in Computer Science, software engineering, Machine Learning, or a related field.
- Strong programming skills in Python (PyTorch, TensorFlow, Hugging Face, LangChain, FastAPI, Flask).
- Good understanding of ML model architecture and LLMs, including how they are trained, fine-tuned, and deployed on AWS platform.
- Familiarity with distributed model training and model optimization.
- Experience deploying ML models and LLMs in cloud environments and local environments.
- Proficiency with AWS infrastructure, including EC2, S3, SageMaker and Bedrock.
- Ability to build effective ML pipelines for research and development.
- Experience with ML model lifecycle tools (e.g., MLflow, DVC, Weights & Biases).
- Proficiency with DevOps/MLOps best practices, including CI/CD, version control (Git), docker and IaC.
- Excellent problem-solving skills, with the ability to troubleshoot performance bottlenecks in ML pipelines.
- Fluent in English, with the ability to communicate complex technical topics effectively.
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