UPSD Lead AI Architect

Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow—people with a unique combination of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level.

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.

• 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 Lang Chain, Llama Index, CrewAI, Auto Gen, 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.

• 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, Lang Chain, Llama Index, CrewAI, and Auto Gen.
• Strong proficiency in Python with deep experience in Pandas, PySpark, Tensor Flow, 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.
• Stro…