Full Stack AI Engineer (m/f/d)

Join us!

At IntegrityNext, we are building a shared AI and data platform on AWS on top of our supply chain and product compliance platform. This platform will power semantic data access, BI, APIs, and agentic product experiences across our product landscape.

As AI Application Engineer (m/f/d), you will build and ship AI-powered product capabilities end-to-end. You will work hands-on across Python-based AI services, agent workflows, web and document retrieval pipelines, unstructured data processing, APIs, integrations, and lightweight React prototypes where needed.

Our AI application layer is built around LangChain, LangGraph, Deep Agents, and Amazon Bedrock AgentCore. The focus is on product delivery rather than research, covering both structured-data-driven AI experiences and unstructured-data processing for retrieval, agent workflows, and future AI-native product capabilities.

We work according to “You build it, you run it”, own capabilities from design to production operations, follow spec-driven development, and actively use AI-assisted engineering tools such as Claude Code, Cursor, and similar tools.

What can you expect?

Build agentic AI product capabilities

• Build AI-powered product features using retrieval mechanisms, workflows, tools, and multi-step agent patterns
• Turn product and business needs into reusable, reliable AI capabilities for internal and customer-facing use cases
• Implement AI capabilities in Python using LangChain, LangGraph, Deep Agents, and Bedrock AgentCore
• Design and build AI APIs with clean service boundaries for integration into Java-based product systems

Work with structured, unstructured, web, and document data* Build and maintain retrieval and unstructured-data processing pipelines for documents, web content, and other sources used in agent workflows

• Work with content and PDF extraction, link following, candidate ranking and selection, deduplication, and source or provenance tracking
• Collaborate with platform and semantic teams to ground AI features in reliable, well-structured data
• Support AI experiences based on structured business data, semantic layers, and analytics-driven use cases

Improve AI quality, reliability, and production readiness* Drive prompt engineering and context engineering to improve quality, reliability, and usability

• Implement evals, regression checks, prompt-injection tests, scenario-based validation, and quality controls
• Help define evaluation harnesses, testing approaches, and validation methods for agentic and retrieval systems
• Support AI observability, tracing, quality monitoring, maintainability, and production operations

Contribute across product and engineering layers* Contribute to lightweight frontend prototypes and conversational or workflow-driven UI flows in React where needed

• Work closely with semantic, data platform, architecture, product, and engineering teams
• Help establish practical spec-driven and AI-assisted engineering workflows that improve quality, speed, and maintainability

What should you bring along?

Experience & Domain Focus

• Strong practical experience with Python and proven experience building AI-powered product features in production or near-production environments
• Experience with LLM application development, including agentic RAG, tool use, workflow orchestration, prompt engineering, and context design
• Strong knowledge of agentic AI concepts such as agent workflows, planning patterns, tool usage, multi-step reasoning architectures, and agent harnesses
• Experience building multi-step agents or pipelines that retrieve, read, and reason over information from web and document sources with a focus on grounding, attribution, and reliability
• Experience building or maintaining unstructured-data pipelines for AI applications, including document and web-page processing, text extraction, retrieval, and AI-oriented data prepa