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Exiger is an AI-native platform for supply chain and procurement automation, serving 550+ global customers including 150 Fortune 500 companies and 80+ government and defense organizations. The company is FedRAMP authorized and a 2x Gartner Magic Quadrant Leader for Supplier Risk Management.
This role is designed for an engineer who wants to move deeper into product thinking. You'll combine technical building skills with product judgment—deciding not just how to build, but what to build and why it matters to customers.
As an AI Product Builder, you will turn customer and business problems into prototypes, experiments, automations, and live product features. You'll work end-to-end across application, data, and AI layers, using large language models, AI agents, automation tools, code, and rapid prototyping. Key responsibilities include:
- Building AI-powered applications and workflows using LLMs, APIs, data, code, and low-code tools
- Designing and orchestrating AI agents that use tools, retrieve information, perform tasks, and operate within larger workflows
- Creating AI automations that reduce repetitive work and improve process speed, ease, or accuracy
- Experimenting with prompts, context, retrieval strategies, tool use, model selection, and parameters to improve outcomes
- Building evaluations and test cases to measure quality, reliability, accuracy, safety, and usefulness of AI features
- Rapidly prototyping ideas, gathering user feedback, and iterating
- Contributing directly to scoped production features with support from experienced engineers
- Connecting applications to APIs and data sources; understanding information flow through systems
- Investigating unexpected behavior, debugging problems, and partnering with engineering on solutions
- Talking directly with customers and internal users to understand their work and identify opportunities
- Using product data and user feedback to decide what to build, improve, or stop
- Documenting experiments, decisions, and results for team learning
- Applying sound judgment around security, privacy, intellectual property, and human oversight in AI systems
You'll work in an AI-enabled product development lifecycle where AI accelerates research, exploration, analysis, code generation, testing, and documentation—but human judgment and accountability remain central. You'll have room to experiment, access to experienced collaborators, and opportunities to take on increasingly complex work as your skills grow.
Success in the first year means developing practical understanding of Exiger's customers, platform, and AI practices; shipping useful prototypes and product improvements; progressing from scoped features to independently leading well-defined product problems; demonstrating ability to select and configure appropriate AI models and tools; building measurable, reliable AI experiences grounded in customer needs; and earning teammate trust through curiosity, technical contribution, and follow-through.
REQUIREMENTS:
- Have built and shipped something real: an application, AI agent, automation, website, internal tool, data project, prototype, open-source contribution, or other working product
- Understand LLM fundamentals including capabilities and limitations; have experimented with improving results
- Experience with or strong interest in: agent orchestration, tool calling, retrieval-augmented generation (RAG), prompt and context design, model evaluation, model selection, or parameter tuning
- Can write code in Python, JavaScript, or TypeScript and want to continue developing engineering skills
- Familiarity with APIs, SQL or data structures, Git, testing, and debugging
- Comfortable starting with unclear problems and figuring out what to try
- Comfortable sharing early work, receiving feedback, and improving quickly
- Can explain technical ideas clearly to people with different backgrounds
- Care about usefulness to customers, not just technical impressiveness
- Take ownership and know when to ask for help
Experience Level: Designed for early-career professionals with typically one to three years of relevant experience. Relevant experience may come from full-time employment, internships/co-ops, academic or independent research, startup work, open-source contributions, hackathons, freelance work, or substantial personal projects. Bachelor's degree in computer science, engineering, data science, information systems, product design, business, or related field is helpful but equivalent practical experience is equally welcome.
Helpful but not required: experience building applications with commercial or open-source language models; familiarity with AI agent frameworks, vector databases, embedding models, or evaluation tools; experience deploying applications or operating something used by real users; exposure to cloud platforms and modern software development practices; experience with B2B SaaS, enterprise software, supply chain technology, risk management, or compliance; familiarity with Figma, Jira, Confluence, or Productboard.
