Among computer occupations, all other, AI is already used for a large share of the tasks. Measured exposure for computer occupations, all other: 31% (high).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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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.

Job Description:

Position Summary:

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.

Key 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.

Qualifications:

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.

This role is located in Alpharetta, GA with a hybrid work schedule. In office Monday- Thursday.

Employee Type: Permanent

UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.

Other Criteria:

UPS is an equal opportunity employer. UPS does not discriminate on the basis of race/color/religion/sex/national origin/veteran/disability/age/sexual orientation/gender identity or any other characteristic protected by law.

Basic Qualifications:

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.