Senior ML Engineer

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.
  
  

• 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.
• Experience working with Apache Spark to process large-scale distributed datasets.
• Experience working with the Databricks platform.
• 5+ years of customer-facing experience in a pre-sales or post-sales role.
• Can meet expectations for technical training and role-specific outcomes within 3 months of hire.