Lead AI Architect - LLM, RAG & Agentic AI

Ups Job

Alpharetta (GA)

Hybrid

USD 140,000 - 190,000

Full time

14 days+

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Job summary

UPS in Alpharetta, GA seeks a Lead AI Architect to design, develop, and deploy enterprise-scale AI and Generative AI solutions. The candidate will lead cross-functional teams, drive LLM-powered applications, RAG architectures, and agent-based workflows.

You will partner with data scientists and engineers to implement scalable APIs, vector databases, cloud platforms, and PEFT techniques, delivering measurable KPI improvements while ensuring safety and governance.

Qualifications

3+ years of experience in traditional AI methodologies including deep learning, supervised and unsupervised learning, and NLP techniques.
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.

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.