Position Responsibilities:
* Technical Leadership and Strategy:
o Develop and manage the Generative AI roadmap.
o Identify critical business problems that can be solved using Generative AI.
o Define and implement strategies for leveraging Generative AI within the organization.
o Lead discussions in peer reviews and use quantitative skills to influence decision-making
positively.
* Solution Architecture and Design:
o Ensure the appropriate level of LLM complexity for various use cases (e.g., Dolly vs. GPT4).
o Create technical standards and blueprints for Generative AI scenarios.
o Lead prompt engineering efforts to optimize LLM performance.
o Quickly prototype and test LLM scenarios to refine user experiences.
* Advanced Analytics, Data Science, and Machine Learning:
o Strong theoretical background and extensive experience in machine/deep learning,
generative AI, and statistical modeling.
o Spearhead fine-tuning of LLMs and building RAG (Retrieval-Augmented Generation)
systems.
o Develop and embed automated processes for predictive model validation, deployment,
and implementation.
o Influence the AI/ML stack, including Feature Stores, Model Stores, and automated
MLOps, to maximize the value of LLMs.
o Make impactful contributions to internal discussions on emerging machine learning
methodologies.
* Cross-Functional Collaboration:
o Work with cross-functional teams, including data scientists, data engineers, and research
scientists, to deliver features iteratively.
o Lead internal and external developers to execute the Generative AI roadmap.
o Connect and collaborate with subject matter experts across different business areas.
o Educate technical and business leaders on the use of Generative AI.
* Continuous Learning and Innovation:
o 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.
o Report findings clearly and structurally through excellent written and communication
skills.
o 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.
