UPSD Lead AI Architect

UPS Supply Chain Solutions

40d•Hybrid

About The Position

We are seeking a Lead AI Architect to design, develop, and deploy enterprise-scale AI and Generative AI solutions. The ideal candidate will have expertise in LLM-powered applications, agentic AI, RAG architectures, traditional machine learning, and cloud-based AI platforms, while providing technical leadership across cross-functional teams.

Requirements

3+ years of experience in traditional AI methodologies including deep learning, supervised and unsupervised learning, and NLP techniques (e.g., tokenization, named entity recognition, text classification, sentiment analysis).
Production experience building and deploying LLM-powered applications, including prompting, context engineering, agent architectures, evaluation frameworks, RAG pipelines, and vector database integration.
Hands-on experience with agentic frameworks: Hugging Face Transformers, LangChain, LlamaIndex, CrewAI, and AutoGen.
Strong proficiency in Python with deep experience in Pandas, PySpark, TensorFlow, and XGBoost; experience building production-grade applications required.
Extensive experience designing, developing, and deploying RESTful APIs and microservices.
Experience with cloud ML platforms (GCP Vertex AI, Azure ML) and vector databases (Pinecone, Weaviate, Milvus, FAISS).
Knowledge of containerization and orchestration tools (Docker, Kubernetes) and/or full-stack development experience (React, Golang).
Knowledge of prompt tuning, fine-tuning, and parameter-efficient adaptation methods (LoRA, PEFT).
Familiarity with knowledge graph construction and integration with LLM and multi-agent systems.
Strong communication and storytelling skills with the ability to present complex AI concepts to non-technical stakeholders.
Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field; international equivalent or equivalent job experience accepted.
Must be a U.S. Citizen or National of the U.S., an alien lawfully admitted for permanent residence, or an alien authorized to work in the U.S. for this employer.

Responsibilities

Designs, develops, and deploys production LLM-powered applications incorporating prompt engineering, context engineering, Retrieval Augmented Generation (RAG), and agent orchestration frameworks.
Integrates large language models with knowledge graphs and multi-agent systems to solve complex, multi-step business problems.
Architects and implements agentic workflows using frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, and Hugging Face Transformers.
Builds and maintains scalable RESTful APIs and microservices to expose AI capabilities across the organization.
Designs vector database schemas and manages embeddings pipelines using platforms such as Pinecone, Weaviate, Milvus, or FAISS.
Applies traditional AI methodologies including supervised and unsupervised learning, deep learning, and NLP techniques such as tokenization, named entity recognition, text classification, and sentiment analysis.
Develops and executes model evaluation frameworks to assess output quality, safety, and performance of deployed AI systems.
Applies parameter-efficient fine-tuning methods including prompt tuning, LoRA, and PEFT to adapt foundation models for domain-specific applications.
Leverages cloud ML platforms (GCP Vertex AI, Azure ML) and containerization tools (Docker, Kubernetes) to operationalize and scale AI workloads.
Collaborates with data scientists, engineers, and business stakeholders to gather requirements, define scope, and ensure AI solutions align with organizational KPIs and decision-making needs.
Documents AI system architectures, integration patterns, and deployment strategies to support knowledge sharing and organizational learning.