Sr Data Scientist / Machine Learning Engineer

Job Description Key Accountabilities: Partner with fellow Data Scientists, ML engineers, MLOps / DevOps engineers and cross-functional teams to solve complex problems and create unique solutions using modern NLP technologies, particularly LLMs. Build data pipelines and deployment pipelines for ML models. Develop ML models according to business and functional requirements. Help deploy various models and tune them for better performance. Document and communicate design and implementation details. Contribute to the DSE AI team on technical decisions. Collaborate with clients and informatics departments to deploy scalable and easy-to-maintain solutions. Serve as a technical point of contact for enterprise-wide technology solutions. Lead complex troubleshooting efforts and root cause analysis. Qualifications: Experience with LLM applications development, including tools using and reasoning, such as RAG solutions and code interpreters. Experience with LLM fine-tuning is a big plus. Experience in building data pipelines and deployment pipelines for LLM applications. Recent experience with ML/AI toolkits such as AWS Sagemaker (other toolkits like Pytorch, Tensorflow, Keras, MXNet, H2O, etc. are nice to have). Experience with MLOps technologies (Sagemaker, Vertex AI, Kubeflow). Experience with cloud solutions (AWS / Azure / Google Cloud Platform) and Docker. Proven scripting and automation skills. Good knowledge of: git, bash, Linux, CI/CD tools (e.g., Jenkins, GitLab CI), software lifecycle, RDB, and visualization tools (e.g., Tableau, Jira, Confluence). Programming: Python, R. Test-driven development and good coding practices. Problem-solving and decision-making skills. Good interpersonal skills with a customer delivery focus. Ability to work effectively with team members and virtual teams from different locations and cultural backgrounds. Experience with deployment of scalable apps is a plus. Experience with clinical study data is a plus.

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