Senior Machine Learning Engineer

Role Overview: At DataNimbus, you will be part of a dynamic team focused on leveraging Data and AI to drive growth, innovation, and efficiency. You will have the opportunity to work with cutting-edge technologies, contribute to solutions trusted by global businesses, and grow both personally and professionally in a culture that values curiosity and continuous learning. Key Responsibilities: - Build and increase customer data science workloads and apply the best MLOps to productionize these workloads across a variety of domains. - Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation. - Advise data teams on several data science topics such as architecture, tooling, and best practices. - Provide technical mentorship to the larger ML Subject Matter Expert community. Qualifications Required: - 6+ years of hands-on industry data science experience, using typical machine learning and data science tools including pandas, mlflow, scikit-learn, gensim, nltk, and TensorFlow/PyTorch. - Experience building production-grade machine learning deployments on AWS, Azure, or GCP including drift monitoring. - Experience with the latest techniques in natural language processing including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI. - Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research) or equivalent practical experience. - Experience communicating and teaching technical concepts to non-technical and technical audiences alike. - Passion for collaboration, life-long learning, and driving value through ML. - Can meet expectations for technical training and role-specific outcomes within 3 months of hire. (Note: The additional details of the company were not explicitly mentioned in the job description provided.) Role Overview: At DataNimbus, you will be part of a dynamic team focused on leveraging Data and AI to drive growth, innovation, and efficiency. You will have the opportunity to work with cutting-edge technologies, contribute to solutions trusted by global businesses, and grow both personally and professionally in a culture that values curiosity and continuous learning. Key Responsibilities: - Build and increase customer data science workloads and apply the best MLOps to productionize these workloads across a variety of domains. - Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation. - Advise data teams on several data science topics such as architecture, tooling, and best practices. - Provide technical mentorship to the larger ML Subject Matter Expert community. Qualifications Required: - 6+ years of hands-on industry data science experience, using typical machine learning and data science tools including pandas, mlflow, scikit-learn, gensim, nltk, and TensorFlow/PyTorch. - Experience building production-grade machine learning deployments on AWS, Azure, or GCP including drift monitoring. - Experience with the latest techniques in natural language processing including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI. - Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research) or equivalent practical experience. - Experience communicating and teaching technical concepts to non-technical and technical audiences alike. - Passion for collaboration, life-long learning, and driving value through ML. - Can meet expectations for technical training and role-specific outcomes within 3 months of hire. (Note: The additional details of the company were not explicitly mentioned in the job description provided.)