GenAI and Large Language Model (LLM) Software Engineer

Software Engineer II - GenAI / LLM Engineer

Scope:

• This is a full-stack AI engineering role with a strong focus on building intelligent, multi-agent applications that power supply chain automation at scale. The ideal candidate brings hands-on experience with LLM orchestration frameworks, Retrieval-Augmented Generation (RAG) pipelines, context graphs, and the Azure AI ecosystem. You will collaborate with product engineers, data scientists, and platform architects to design and ship production-grade GenAI capabilities across Blue Yonder's supply chain platform.

What you’ll do:

• Designs, builds, and maintains LLM-powered application components adhering to clean code principles, Blue Yonder engineering standards, and effective test coverage.
• Develops and optimizes Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, vector store management, embedding tuning, and retrieval relevance evaluation.
• Builds and maintains multi-agent orchestration workflows using LangChain, LangGraph, or equivalent agentic frameworks — including inter-agent communication, tool use, memory, and state management.
• Implements context graph solutions to enable richer semantic understanding and cross-entity reasoning within supply chain domains.
• Designs and integrates Azure AI services including Azure OpenAI, Azure AI Search (Cognitive Search), Azure Machine Learning, and related Azure PaaS components.
• Participates actively in team ceremonies — backlog grooming, sprint planning, daily stand-ups, and retrospectives.
• Contributes to prompt engineering best practices, LLM evaluation frameworks, and guardrail strategies for hallucination mitigation.
• Understands how changes to GenAI components affect downstream agents, dependent services, and end-customer experiences.
• Incorporates information security, responsible AI, and data privacy considerations into all development work.
• Identifies production issues with LLM agents and escalates to the team with relevant context (traces, prompts, token usage).
• Autonomously plans and performs routine changes to agent configurations, prompt templates, and model version upgrades.
• Independently resolves incidents within a defined set of GenAI service functions, including model fallback and latency issues.
• Handles service requests related to agent onboarding, tool integration, and knowledge base updates.

What we are looking for:

• 2–5 years of software engineering experience with at least 1–2 years focused on GenAI / LLM application development.
• Strong proficiency in Python and/or TypeScript/JavaScript for building AI-powered backend and frontend services.
• Hands-on experience with LangChain and/or LangGraph for agentic orchestration, tool use, and memory management.
• Solid understanding of RAG architecture: document ingestion, chunking, embedding models, vector databases (e.g., Azure AI Search, Weaviate, Pinecone), and reranking.
• Experience building or working with multi-agent systems — including agent routing, orchestrator/worker patterns, and inter-agent handoffs.
• Working knowledge of the Azure AI ecosystem: Azure OpenAI Service, Azure AI Search, Azure Functions, Azure Container Apps, and related services.
• Familiarity with context graphs, knowledge graphs, or graph-based retrieval for enhancing LLM reasoning.
• Experience with prompt engineering, system prompt design, few-shot prompting, and chain-of-thought techniques.
• Understanding of LLM evaluation strategies: hallucination detection, faithfulness scoring, and output quality metrics.

Our Values

If you want to know the heart of a company, take a look at their values. Ours unite us. They are what drive our success – and the success of our customers. Does your heart beat like ours? Find out here: Core Values

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.

Experience Level

Mid Level