AI Product Builder

Exiger•McLean, VA

1d•Hybrid

About The Position

Exiger is seeking an AI Product Builder to join their team. This role is for an engineer who wants to transition into product development, focusing on turning ideas into functional products. The ideal candidate will have experience building and shipping real products and a desire to develop product judgment. This is an early-career opportunity that requires strong technical foundations, a passion for building, and the ability to learn quickly. The AI Product Builder will work end-to-end across application, data, and AI layers, collaborating with various teams and customers to create AI-powered applications, workflows, and automations using large language models, AI agents, and rapid prototyping. The role emphasizes responsible AI use, continuous learning, and contributing to production features.

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 the fundamentals of large language models, including their capabilities and limitations, and have experimented with getting better results from them.
Experience with—or a 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 a language such as Python, JavaScript, or TypeScript and want to continue developing your engineering skills.
Some familiarity with APIs, SQL or data structures, Git, testing, and debugging.
Enjoy starting with an unclear problem and figuring out what to try.
Comfortable sharing early work, receiving feedback, and improving it quickly.
Can explain technical ideas clearly to people with different backgrounds.
Care about whether something is useful to customers—not simply whether the technology is impressive.
Take ownership of your work and know when to ask for help to move a problem forward.
One to three years of relevant experience (full-time employment, internships or co-ops, academic or independent research, startup work, open-source contributions, hackathons, freelance work, or substantial personal projects).
A bachelor's degree in computer science, engineering, data science, information systems, product design, business, or a related discipline, or equivalent practical experience.

Nice To Haves

Experience building applications with commercial or open-source language models.
Familiarity with AI agent frameworks, vector databases, embedding models, or evaluation tools.
Experience deploying an application 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 product and collaboration tools such as Figma, Jira, Confluence, or Productboard.

Responsibilities

Turn customer and business problems into prototypes, experiments, automations, and live product features.
Build AI-powered applications and workflows using large language models, APIs, data, code, and low-code tools.
Design and orchestrate AI agents that can use tools, retrieve information, perform tasks, and operate within larger workflows.
Create AI automations that reduce repetitive work and make complex processes faster, easier, or more accurate.
Experiment with prompts, context, retrieval strategies, tool use, model selection, and model parameters to improve outcomes for specific use cases.
Build evaluations and test cases to measure the quality, reliability, accuracy, safety, and usefulness of AI-powered features.
Rapidly prototype ideas, put them in front of users, learn from their reactions, and iterate.
Contribute directly to scoped production features with support and review from experienced engineers.
Connect applications to APIs and data sources and understand how information moves through a software system.
Investigate unexpected behavior, debug problems, and partner with engineering to develop reliable solutions.
Talk directly with customers and internal users to understand how they work and identify opportunities to make their jobs easier.
Use product data and user feedback to decide what to build, improve, or stop.
Document experiments, decisions, and results so others can understand and build upon your work.
Use AI responsibly, applying sound judgment around security, privacy, intellectual property, customer data, and human oversight.