Associate Director, Generative AI Engineer

Role Summary

Associate Director, Generative AI Engineer at Madrigal Pharmaceuticals. You will lead the development and implementation of the Generative AI roadmap for enterprise support functions, creating business-facing experiences that showcase the value of LLMs to HR, Finance, IT, Legal, and Compliance operations.

Responsibilities

• Leadership and Strategy:
  • Develop and oversee the Generative AI roadmap in collaboration with senior leadership, ensuring alignment with organizational priorities.
  • 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
  • Lead and manage a team of AI engineers, data scientists, and developers, providing mentorship, coaching, and career development support
  • Establish processes for hiring, onboarding, and performance management of AI talent
• 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

• Advanced degree (Ph.D. preferred) in Engineering, Statistics, Data Science, Applied Mathematics, Computer Science, Physics, Bioinformatics, or 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
• 2+ years of experience architecting modular multi-agent systems powered by frontier LLMs and leading agent frameworks (LangChain family, AutoGen, CrewAI, etc.), designing secure agent-to-agent communication with shared memory, credential vaults, and RBAC, and implementing enterprise MCP tools and Agent Protocols
• Strong understanding of cloud-based data platforms and technologies (e.g., AWS, Azure, Google Cloud) and their application in building scalable analytics solutions
• Proven ability to build and lead cross-functional teams, set clear priorities, and foster accountability and collaboration to drive organizational success
• Proven experience in machine learning and software engineering best practices
• Demonstrated ability in writing and presenting papers, documentation, and presentations to explain research findings

Education

• Advanced degree (Ph.D. preferred) in a quantitative field as listed above