Intermediate AI Engineer

As an AI Engineer at a Fortune Global 500 organization like UPS, you will have the opportunity to design, develop, and deploy cutting-edge AI systems leveraging Large Language Models (LLMs), Chatbots, Retrieval-Augmented Generation (RAG), and agentic AI architectures. Your role will involve hands-on development with modern frameworks like LangChain, LangGraph, and LlamaIndex. You will be working with talented teams to build scalable, production-grade AI platforms. **Key Responsibilities:** - **Agentic AI Development:** - Design, build, and deploy agentic AI systems using frameworks such as LangChain, LangGraph, and related libraries. - Develop multi-agent systems capable of autonomous decision-making, reasoning, planning, and collaboration. - **RAG Pipelines:** - Implement and optimize retrieval-augmented generation (RAG) systems for grounded, accurate responses. - **LLM Engineering:** - Fine-tune and prompt-engineer LLMs for task-specific reasoning, planning, and dynamic adaptation. - Work with LLM/SLM APIs, embeddings, and advanced generative AI techniques. - **Enterprise AI Platform:** - Lead the development of enterprise-grade AI platforms integrating LLMs, RAG, embeddings, and agentic AI protocols. - Implement Model Context Protocol (MCP) for consistent context management across models and agents. - **MLOps & Observability:** - Establish best practices for MLOps, monitoring, and observability for scalable and maintainable AI solutions. - **Applied AI Prototyping:** - Rapidly prototype, experiment, and iterate to improve AI agent capabilities. - **Collaboration & Research:** - Participate in the full research cycle, collaborate effectively with other engineers, researchers, and data scientists, and contribute to documentation and standardization of technical code and practices. **Qualifications Required:** - Bachelors degree in Computer Science, Engineering, or a related quantitative field. Masters or Ph.D. is a strong plus. - 5+ years overall experience in software development, data science, or machine learning. - 1+ year of hands-on experience developing AI applications with LLMs and systems such as retrieval-based methods, fine-tuning, or agent-based architectures. - Strong programming skills in Python, basics in SQL, expertise with LLM/SLM APIs, embeddings, and RAG systems. - Experience deploying on Google Cloud Platform (GCP) with Vertex AI, and IBM WatsonX. - Familiarity with agentic AI protocols and exposure to Agent Development Kits (ADKs). In this permanent role at UPS, you will have the opportunity to work with talented teams, experience a rewarding culture, and contribute to the innovative possibilities that will lead UPS into the future. UPS is committed to providing a workplace free of discrimination, harassment, and retaliation. As an AI Engineer at a Fortune Global 500 organization like UPS, you will have the opportunity to design, develop, and deploy cutting-edge AI systems leveraging Large Language Models (LLMs), Chatbots, Retrieval-Augmented Generation (RAG), and agentic AI architectures. Your role will involve hands-on development with modern frameworks like LangChain, LangGraph, and LlamaIndex. You will be working with talented teams to build scalable, production-grade AI platforms. **Key Responsibilities:** - **Agentic AI Development:** - Design, build, and deploy agentic AI systems using frameworks such as LangChain, LangGraph, and related libraries. - Develop multi-agent systems capable of autonomous decision-making, reasoning, planning, and collaboration. - **RAG Pipelines:** - Implement and optimize retrieval-augmented generation (RAG) systems for grounded, accurate responses. - **LLM Engineering:** - Fine-tune and prompt-engineer LLMs for task-specific reasoning, planning, and dynamic adaptation. - Work with LLM/SLM APIs, embeddings, and advanced generative AI techniques. - **Enterprise AI Platform:** - Lead the development of enterprise-grade AI platforms integrating LLMs, RAG, embeddings, and agentic AI protocols. - Implement Model Context Protocol (MCP) for consistent context management across models and agents. - **MLOps & Observability:** - Establish best practices for MLOps, monitoring, and observability for scalable and maintainable AI solutions. - **Applied AI Prototyping:** - Rapidly prototype, experiment, and iterate to improve AI agent capabilities. - **Collaboration & Research:** - Participate in the full research cycle, collaborate effectively with other engineers, researchers, and data scientists, and contribute to documentation and standardization of technical code and practices. **Qualifications Required:** - Bachelors degree in Computer Science, Engineering, or a related quantitative field. Masters or Ph.D. is a strong plus. - 5+ years overall experience in software development, data science, or machine learning. - 1+ year of hands-on experience developing AI applications with LLMs and systems such as retrieval-b