Data Scientist II - ML Engineering

Lead ML engineering across an enterprise ML platform, production model lifecycle, and Generative AI workflows.

Responsibilities

• Design, create, and maintain an ML platform and related environments
• Manage Docker containers and Kubernetes clusters, including dependencies and configurations
• Implement CI/CD pipelines to automate building, testing, and deployment of machine learning models
• Monitor and optimize model training performance and resource usage
• Deploy machine learning models to production environments
• Manage model versioning and rollback mechanisms
• Enable scalable, reliable model serving using tools such as Vertex, Databricks, TensorFlow Serving, Flask, or FastAPI
• Work with data scientists, data engineers, and stakeholders to understand and fulfill infrastructure needs
• Stay current on latest ML infrastructure technologies and best practices
• Architect and develop Generative AI solutions using Machine Learning and GenAI techniques
• Engineer and deploy Generative AI models focused on Retrieval-Augmented Generation (RAG), search, knowledge graphs, and multi-agent workflows
• Prepare both unstructured and structured data for Language Model Learning (LLM) context, including:
  • Embedding large text corpora
  • Developing generative SQL queries
  • Building connectors to structured databases
• Train models on prepared data and tune fine-tuning hyperparameters for optimal performance

Analytics / Design & Development

• Build a framework to stitch cross-domain learning and optimize for mission-specific and multi-mission tasks
• Specialize in AI interpretation and causality:
  • Uncover model causality relationships
  • Build a framework to measure bias, underspecification, and latent drivers with their connections
• Create an enterprise domain-specific reasoning system to improve actionable insights and optimize machine learning resource usage
• Orchestrate reusable storytelling methodology to support AI translation
• Apply inquisitive, transparency-focused approaches for ML/AI visibility to the business
• Use AI reasoning to produce business action recommendations
• Apply AI research to accelerate business innovation

Requirements

• Related degree or comparable formal training, certification, or work experience
• 5+ years of experience in a retail or retail-related decision science role
• Expertise in ML visualization flow
• Expertise in optimizing distributed machine learning in a heterogeneous domain environment
• Programming knowledge: SQL, R, Python, Scala, Java, C/C++
• Big data / ML optimization knowledge, including:
  • GPU code optimization
  • Horovod
  • Spark MLlib optimization
  • Cython, JNI, Numba
• Mainstream ML / AI knowledge, including:
  • Manifold learning
  • Distributed clustering
  • Graph network
  • Hierarchical model
  • Bayesian network
  • Deep learning
  • Computer vision
  • NLP/NLU
  • Reinforcement learning
  • Meta-learning
  • Federated learning
• Skills to apply causal reasoning representation and learning, plus human-centric, explainable, responsible AI
• Ability to act as a creative storyteller and translator between business questions and ML solutions
• Ability to work with imperfect or incomplete data
• Ability to apply AI reasoning into business action recommendation
• Comfort working in a fast-paced retail environment with frequently shifting priorities
• Ability to work extended hours and sit for long periods

Technologies

• Docker, Kubernetes, CI/CD
• Vertex, Databricks, TensorFlow Serving, Flask, FastAPI
• Machine Learning, GenAI, Retrieval-Augmented Generation (RAG), Language Model Learning (LLM)
• SQL, R, Python, Scala, Java, C/C++
• GPU code optimization, Horovod, Spark MLlib, Cython, JNI, Numba
• Manifold learning, distributed clustering, graph network, hierarchical model, Bayesian network
• Deep learning, computer vision, NLP/NLU, reinforcement learning, meta-learning, federated learning

Location: San Antonio, TX (onsite)

Experience: 5+ years