About the role
About Complete iQ Complete iQ builds AI-driven software for the insurance claims industry. Our platform ingests claim documents, runs OCR and vision/LLM extraction, structures the data, and performs cross-document analysis so carriers and adjusters can process claims faster and more accurately. AI isn't a feature bolted onto our product — it is the product. We're a small team growing fast, and the work you ship reaches real users in days, not quarters.
The Role We're hiring a Solutions Engineer to own the technical and AI layer between our product and the carriers who use it. This is a hands-on, customer-adjacent role: you'll configure new carrier setups, engineer and tune the prompts that drive our extraction and analysis, QA the AI's output against real claim documents, build and run product demos, and act as the technical bridge between customers, sales, and engineering. You won't be writing core product code — but you will live in prompts, model behavior, JSON configs, and test cases every day. We're growing fast, which means the role is fluid and the playbook is still being written — often by you. One week you're tuning extraction prompts for a new carrier; the next you're inventing a demo flow that didn't exist, debugging why a model missed a field, and writing the QA process you wish we already had. This is the right role for someone who is technical, genuinely curious about AI, creative under ambiguity, and energized rather than rattled when the ground keeps shifting. If you need a fully defined process before you can move, this isn't it. If you like building the process as you move, read on. What you'll do Prompt engineering & AI tuning — Design, test, and refine the prompts and extraction logic that drive the platform's AI. Iterate on model behavior to push accuracy on real-world, messy claim documents. Carrier configuration & onboarding — Set up new carriers on the platform: build classification and extraction configs, map document types and fields, and validate that AI output matches what the carrier expects. AI/product QA — Test extraction accuracy, classification logic, and cross-document analysis against real claim documents. Catch regressions and model failure modes, document them clearly, and verify fixes before they reach customers. Sales & solution demos — Build and run technical demos for carriers and prospects, lead technical discovery, and answer the "can the AI do X?" questions that come up in the sales cycle — often inventing the demo as you go. Engineering interface — Translate customer needs and field issues into clear, reproducible reports for the engineering team, and validate solutions before they ship. Be the person who can explain the product to a customer and the customer to the engineers. Tooling & light scripting — Write scripts, configs, and test automation that make onboarding, prompt evaluation, and QA faster and more repeatable. Feedback loop — Surface patterns from the field (recurring config needs, common AI failure modes, feature gaps) to influence what we build next. You'll often define the process, not just follow it.
What we're looking for 3–5 years in a technical, customer-adjacent role: solutions/sales engineering, implementation, technical support engineering, QA, or similar. Genuine curiosity about and hands-on experience with AI/LLMs — you've tuned prompts, evaluated model output, or built something with AI tools and understand how they fail, not just how they demo. Comfortable reading and writing structured data (JSON), writing scripts (Python, JavaScript, or similar), and navigating APIs. You don't need to ship production application code — you do need to be genuinely technical. Strong QA instincts: methodical, detail-obsessed, good at finding the edge case that breaks things and documenting it so someone else can reproduce it. Creative and resourceful under ambiguity — you figure out the next move when no one hands you a playbook, and you build the process as you go. High ownership and drive. We're scaling fast; priorities shift, hats get swapped, and the person who thrives here treats that as the fun part rather than the problem. Clear communicator who can run a demo, lead a technical conversation with a customer, and write a crisp bug report for engineering. Nice to have Experience in insurance, P&C, claims, or insurtech (strongly preferred — it shortens your ramp significantly). Hands-on prompt engineering or experience configuring/evaluating AI-driven systems in production. Familiarity with OCR, vision models, document processing, or structured data extraction. Experience with SQL / PostgreSQL. Early-stage or high-growth startup experience. Details Location: Remote. Level: Mid-level. Type: Full-time. Complete iQ is an equal opportunity employer. We welcome applicants from all backgrounds.
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About the role
About Complete iQ Complete iQ builds AI-driven software for the insurance claims industry. Our platform ingests claim documents, runs OCR and vision/LLM extraction, structures the data, and performs cross-document analysis so carriers and adjusters can process claims faster and more accurately. AI isn't a feature bolted onto our product — it is the product. We're a small team growing fast, and the work you ship reaches real users in days, not quarters.
The Role We're hiring a Solutions Engineer to own the technical and AI layer between our product and the carriers who use it. This is a hands-on, customer-adjacent role: you'll configure new carrier setups, engineer and tune the prompts that drive our extraction and analysis, QA the AI's output against real claim documents, build and run product demos, and act as the technical bridge between customers, sales, and engineering. You won't be writing core product code — but you will live in prompts, model behavior, JSON configs, and test cases every day. We're growing fast, which means the role is fluid and the playbook is still being written — often by you. One week you're tuning extraction prompts for a new carrier; the next you're inventing a demo flow that didn't exist, debugging why a model missed a field, and writing the QA process you wish we already had. This is the right role for someone who is technical, genuinely curious about AI, creative under ambiguity, and energized rather than rattled when the ground keeps shifting. If you need a fully defined process before you can move, this isn't it. If you like building the process as you move, read on. What you'll do Prompt engineering & AI tuning — Design, test, and refine the prompts and extraction logic that drive the platform's AI. Iterate on model behavior to push accuracy on real-world, messy claim documents. Carrier configuration & onboarding — Set up new carriers on the platform: build classification and extraction configs, map document types and fields, and validate that AI output matches what the carrier expects. AI/product QA — Test extraction accuracy, classification logic, and cross-document analysis against real claim documents. Catch regressions and model failure modes, document them clearly, and verify fixes before they reach customers. Sales & solution demos — Build and run technical demos for carriers and prospects, lead technical discovery, and answer the "can the AI do X?" questions that come up in the sales cycle — often inventing the demo as you go. Engineering interface — Translate customer needs and field issues into clear, reproducible reports for the engineering team, and validate solutions before they ship. Be the person who can explain the product to a customer and the customer to the engineers. Tooling & light scripting — Write scripts, configs, and test automation that make onboarding, prompt evaluation, and QA faster and more repeatable. Feedback loop — Surface patterns from the field (recurring config needs, common AI failure modes, feature gaps) to influence what we build next. You'll often define the process, not just follow it.
What we're looking for 3–5 years in a technical, customer-adjacent role: solutions/sales engineering, implementation, technical support engineering, QA, or similar. Genuine curiosity about and hands-on experience with AI/LLMs — you've tuned prompts, evaluated model output, or built something with AI tools and understand how they fail, not just how they demo. Comfortable reading and writing structured data (JSON), writing scripts (Python, JavaScript, or similar), and navigating APIs. You don't need to ship production application code — you do need to be genuinely technical. Strong QA instincts: methodical, detail-obsessed, good at finding the edge case that breaks things and documenting it so someone else can reproduce it. Creative and resourceful under ambiguity — you figure out the next move when no one hands you a playbook, and you build the process as you go. High ownership and drive. We're scaling fast; priorities shift, hats get swapped, and the person who thrives here treats that as the fun part rather than the problem. Clear communicator who can run a demo, lead a technical conversation with a customer, and write a crisp bug report for engineering. Nice to have Experience in insurance, P&C, claims, or insurtech (strongly preferred — it shortens your ramp significantly). Hands-on prompt engineering or experience configuring/evaluating AI-driven systems in production. Familiarity with OCR, vision models, document processing, or structured data extraction. Experience with SQL / PostgreSQL. Early-stage or high-growth startup experience. Details Location: Remote. Level: Mid-level. Type: Full-time. Complete iQ is an equal opportunity employer. We welcome applicants from all backgrounds.