Principal Generative AI Engineer

• Technical Leadership and Strategy:
  • Develop and manage the Generative AI roadmap.
  • Identify critical business problems that can be solved using Generative AI.
  • Define and implement strategies for leveraging Generative AI within the organization.
  • Lead discussions in peer reviews and use quantitative skills to influence decision-making positively.
• Solution Architecture and Design:
  • Ensure the appropriate level of LLM complexity for various use cases (e.g., Dolly vs. GPT-4).
  • Create technical standards and blueprints for Generative AI scenarios.
  • Lead prompt engineering efforts to optimize LLM performance.
  • Quickly prototype and test LLM scenarios to refine user experiences.
• Advanced Analytics, Data Science, and Machine Learning:
  • Strong theoretical background and extensive experience in machine/deep learning, generative AI, and statistical modeling.
  • Spearhead fine-tuning of LLMs and building RAG (Retrieval-Augmented Generation) systems.
  • Develop and embed automated processes for predictive model validation, deployment, and implementation.
  • Influence the AI/ML stack, including Feature Stores, Model Stores, and automated MLOps, to maximize the value of LLMs.
  • Make impactful contributions to internal discussions on emerging machine learning methodologies.
• Cross-Functional Collaboration:
  • Work with cross-functional teams, including data scientists, data engineers, and research scientists, to deliver features iteratively.
  • Lead internal and external developers to execute the Generative AI roadmap.
  • Connect and collaborate with subject matter experts across different business areas.
  • Educate technical and business leaders on the use of Generative AI.
• Continuous Learning and Innovation:
  • Demonstrate a combination of business focus, strong analytical and problem-solving skills, and programming knowledge to quickly cycle hypotheses through the discovery phase of projects.
  • Report findings clearly and structurally through excellent written and communication skills.
  • Stay updated with the latest advancements in Large Language Models (LLMs) and apply them to business scenarios.

Qualifications and Skills Required:

• Advanced degree (Ph.D. preferred) in Engineering, Statistics, Data Science, Applied Mathematics, Computer Science, Physics, Bioinformatics, or a related quantitative field.
• 10+ years of proficiency in Python, SQL, R, MATLAB, PyTorch, Keras, and git.
• 8+ years of experience in ML/deep learning, including hands-on experience with LLM fine-tuning and/or training (e.g., ChatGPT, BERT, Bard, LLaMA, Dolly).
• 8+ years of experience in data visualization and creating dashboards/web applications using Python and R-based tools (Dash, Streamlit, Shiny).
• 8+ years of experience in data manipulation, integration, writing complex queries, and creating data products.
• 8+ years of implementing AI/ML systems using platforms like Databricks or Dataiku.
• Strong understanding of cloud-based data platforms and technologies (e.g., AWS, Azure, Google Cloud) and their application in building scalable and efficient analytics solutions.
• Proven experience in machine learning and software engineering best practices.
• Demonstrated ability in writing and presenting papers, documentation, and presentations to explain research findings.
• Fluency in English.

Compensation:
Base salary is determined by several factors that include, but are not limited to, a successful candidate's qualifications, skills, education, experience, business needs, and market demands. The role may also be eligible for bonus, equity, and comprehensive benefits, which include flexible paid time off (PTO), medical, dental, vision, and life and disability insurance.
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