AI Builders

Brillio

1d•$1 - $1

About The Position

Brillio is an AI-first technology company, backed by private equity, Bain Capital and Orogen Group, partnering with enterprise organizations to drive AI-led transformation at scale. We focus on industries where technology directly impacts growth financial services, healthcare, consumer, telecom and technology. To make that happen, we work closely with executive teams and operate alongside our clients to deploy AI into core business systems moving from strategy to execution. Our Team We bring together three distinct layers of capability: AI-Native Engineers: Engineers, Architects and Product leaders who are native to AI systems designing, building, and scaling production-grade solutions. Leadership: Leaders who have delivered large-scale, global transformation programs — combining business insight with execution discipline Board & Advisors: Providing strategic oversight, governance, and perspective across complex enterprise environments

Requirements

8+ years of healthcare industry experience: Direct, hands-on experience in health insurance operations (underwriting, claims adjudication, utilization management, product launch) or health system clinical and operational workflows not adjacent or observational. You must understand how these environments actually work.
Proficiency with Claude (Anthropic) as a development tool. Demonstrated use of the Claude API including prompt engineering, tool use, and agentic patterns to build real applications. Using Claude as a chat interface does not qualify. You must have built with it.
Full-stack AI engineering capability: Ability to build and ship end-to-end AI applications independently. LLM integration, retrieval-augmented generation (RAG), agentic orchestration, REST API development, and lightweight front-end interfaces sufficient to put a working demo in front of a client.
Demonstrated ability to prototype at speed: Proven track record of moving from a business conversation to a working AI prototype in days not sprints or quarters. You can show examples of what you built, how fast, and what problem it solved.
Business articulation and stakeholder communication: Ability to walk into a meeting with healthcare executives and explain what you built, why it matters, what it costs, and what comes next clearly, confidently, and without jargon. This is a non-negotiable for a client-embedded role.
Maturity to engage at the executive level: Comfortable participating in steering committees, executive briefings, and business rhythm meetings without needing hand-holding. You can hold the room, handle pushback, and demonstrate progress in business leaders care about.
Ability to structure ambiguous problems: When a business leader says 'we need to reduce risk in our underwriting process,' you know how to break that into a scoped AI opportunity with clear inputs, outputs, and success criteria before opening a laptop.
Quantitative fluency for ROI and impact measurement: Ability to build simple but credible business cases estimating time saved, cost reduced, or revenue protected and translate AI outputs into financial terms that resonate with finance and operations leaders.
A builder first : you are most energized when something tangible exists that didn't exist before
Equally fluent in the language of healthcare operations and the language of AI engineering and able to switch between them in the same meeting
Someone who earns trust through working software, not promises you show progress, you don't just report it
Deeply curious about what AI can do in healthcare and intellectually honest about where it falls short
Comfortable with ambiguity, urgency, and high stakes you thrive when the path is unclear and the expectation is still high

Nice To Haves

Experience with agentic AI frameworks: Hands-on use of LangChain, LangGraph, CrewAI, AutoGen, or equivalent orchestration frameworks to build multi-step, tool-calling AI agents that operate in real workflows.
Healthcare data standards and regulatory context : Working familiarity with HL7, FHIR, ICD-10, CPT, and claims data structures. Understanding HIPAA requirements, CMS regulations, and FDA digital health guidance as they apply to AI system design and deployment.
Prior forward-deployed, embedded, or startup engineering experience: You have worked directly inside a client organization, operated in a consulting or embedded delivery model, or built products in a startup environment where you owned the full stack with limited support.
Experience shipping AI in health insurance technology company or health system: You have navigated the additional complexity of deploying AI where compliance, audit trails, explainability, and data governance are not optional and you know how to move fast without cutting corners that matter.
Experience contributing to reusable AI platforms or accelerators: You have built solutions with an eye toward reuse creating components, patterns, or frameworks that others on a team can adopt rather than rebuilding from scratch on every engagement.
Knowledge of AI governance and responsible AI practices in enterprise settings: Familiarity with model risk management, bias evaluation, audit logging, and enterprise AI governance frameworks and experience working alongside governance or compliance teams to get AI systems approved for production use.

Responsibilities

Engage directly with business stakeholders, VPs, clinical leads, operations owners to surface ambiguous, high-value problems and translate them into buildable AI opportunities
Ask sharp questions, challenge assumptions, and define what a minimum viable AI solution looks like before writing a line of code
Map existing processes end-to-end, identify where AI can eliminate friction, reduce cost, or accelerate decisions and communicate your findings in business terms
Build lightweight scoping documents and ROI estimates that align stakeholders and set clear success criteria before development begins
Design and build working AI prototypes within days using Claude and the modern AI development stack
Develop full-stack AI applications spanning LLM orchestration, retrieval-augmented generation (RAG), agentic workflows, REST API integrations, and lightweight front-end interfaces
Own your technical architecture end-to-end from prompt design to data pipeline to user interface and be able to explain every design decision to both engineering and business audiences
Iterate based on real user feedback in the field ship early, learn fast, and drive toward production-grade quality with intention and speed
Use Claude as a core development accelerator for code generation, testing, documentation, and solution design not just as a product capability
Partner with Brillio's AI Governance team to validate, harden, and evolve prototypes into production-grade, enterprise-ready systems.
Apply responsible AI principles bias review, explainability, HIPAA compliance, audit logging, and model monitoring as a standard part of every build.
Document solutions clearly and completely so they can be handed off, scaled, and maintained by broader engineering teams without rework.
Contribute to Brillio's reusable AI accelerator library turning client-specific solutions into patterns that can be deployed across engagements.
Participate in all client business rhythm meetings sprint reviews, program steering committees, and executive status updates
Demonstrate progress through live demos and working software not slide decks alone
Communicate technical concepts clearly to non-technical business leaders and build the trust required to move from pilot to scale
Surface blockers, dependencies, and risks early with clear, solutions-oriented framing
Develop genuine expertise in your client's business, their workflows, their data, their competitive pressures, and their regulatory environment
Understand healthcare operations deeply enough to generate your own problem hypotheses not just respond to inbound requests
Build relationships with business counterparts that go beyond project delivery, becoming a trusted advisor on what AI can and cannot do in their environment
Contribute to internal knowledge sharing bring learnings from client engagements back to Brillio's broader applied AI community