\\n \\n We are looking for an ML Engineer to support AI research and development, focusing on experimenting, deploying and scaling ML models (especially LLMs). As part of our AI Core Team, you will enable cutting-edge research to transition smoothly into production-ready AI features. You will work at the intersection of AI research and engineering, ensuring that ML models can be efficiently deployed, tested, and iterated.The impact you will haveYou will pave the way for AI innovation by developing efficient, scalable, and reliable ways to deploy and manage machine learning models. Your work will enable our AI researchers and software engineers to iterate faster, explore new ideas, and bring AI-powered features into Sonar products. By optimizing the end-to-end ML lifecycle, you will directly contribute to the next generation of AI-driven developer tools. \\\\nOn 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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