AI Engineer (Agentic Systems)

About SureBright

Apple Care-like warranty program for every retailer

Tech description:

We’re building an AI-native, agentic platform that powers the full warranty lifecycle: real-time plan quoting at checkout, policy issuance, automated claims intake, adjudication, and fulfillment (repair/replacement/reimbursement), plus internal tooling for operations, compliance, and finance.

The hard problems we work on:

* Agentic workflows with strong guardrails: automating high-volume decisions while preserving auditability, compliance, and a great customer experience
* Real-time dynamic pricing: combining product/merchant/context and other signals to prepare pricing dynamically
* OEM warranty understanding: LLM-based parsing of manufacturer warranties and coverage rules into machine-readable policy logic
* Claims automation: document/intake normalization, fraud/abuse signals, decisioning, vendor routing, and SLA reliability
* Deep commerce integrations: Shopify and other e-commerce/POS surfaces, plus partner APIs, built to be fast, resilient, and easy to adopt

Our stack is cloud-native and product-velocity oriented:

* TypeScript and Python across services and data/ML
* Postgres for core systems of record; Redis for caching/queues where needed
* Event-driven architecture (pub/sub style messaging) for reliability and scalability
* Data warehouse + BI for funnel, cohort, and loss analytics
* LLM + retrieval (vector search) for policy/claims reasoning and internal copilots
* CI/CD, infrastructure-as-code, and security controls designed for regulated workflows (logging, permissions, audit trails)
* AWS, Azure, Claude Code

Job description:

This is a high-ownership “do whatever it takes” role for someone who wants to operate at founder speed, learn the full stack of an insurance/warranty business, and ship work that directly moves revenue, conversion, and retention.

**What you’ll do**\
You will build the agentic layer of our core product: AI systems that reason, take actions, and reliably complete workflows across pricing/underwriting, policy issuance, claims intake, adjudication, fulfillment (repair/replacement/reimbursement), and other parts of the bueinsess.

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**Key responsibilities**

* Design and ship production-grade AI agents that run real business processes (not demos)
* Build agentic architectures: orchestration, tool calling, state machines, memory, permissions, audit trails, human-in-the-loop, and fallback paths
* Own our RAG platform end-to-end: ingestion, chunking, embeddings, retrieval, reranking, citations/grounding, and hallucination mitigation
* Build evaluation and monitoring systems: offline eval sets, regression tests, online metrics, drift detection, and red-team suites
* Implement model optimization: prompt systems, structured outputs, fine-tuning where appropriate, latency/cost optimization, caching, and throughput tuning
* Build core ML systems for warranty/claims: document understanding, extraction, classification, anomaly/fraud signals, decision support, and SLA routing
* Partner tightly with product/ops to translate real workflows into deterministic, testable, compliant automation

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**What you’ll build (examples)**

* Underwriting/pricing agents: real-time quote decisions using merchant/product/context signals with strict guardrails and auditability
* Claims copilot + auto-adjudication engine: intake triage, evidence requests, decision proposals with explanation, vendor routing, reimbursement automation
* OEM warranty parsing system: turn messy manufacturer policies into machine-readable coverage logic
* Internal ops copilots: tooling that reduces manual work and increases consistency across customer support, compliance, and finance

\*\*Requirements (must have)\*\*\
**(Hiring at different levels for the same role - required experience years, expected skill level will vary as per role level)**

* 1+ years building and shipping ML/LLM systems in production (or equivalent founder-level experience)
* Proven experience building agentic products/companies: multi-step workflows, tool use, orchestration, reliability engineering
* Deep hands-on expertise in:
* RAG and retrieval systems (vector databases, reranking, grounding strategies)
* LLM evals (golden sets, automated judging, human eval, regression pipelines)
* Prompting and structured outputs (schemas, function/tool calling, robustness)
* Model training/fine-tuning fundamentals and tradeoffs (when to tune vs prompt vs retrieve)
* Strong software engineering: clean APIs, testing, observability, performance tuning, secure-by-default design
* Comfortable owning ambiguous problems end-to-end and driving them to measurable outcomes

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**Strong preference (nice to have)**

* Experience building systems with compliance/audit requirements (fintech/insurance/health/enterprise)
* Experience with document AI at scale (PDFs, images, messy inputs), and extracting structured truth reliably
* Experience designing human-in-the-loop workflows and escalation rules for high-stakes decisions
* Experience with infra for LLMs: model hosting, batching, streaming, caching, prompt/version management
* Startup or ex-founder background, especially shipping 0→1 products fast

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**What success looks like (first 90 days)**

* You ship an agentic workflow that replaces meaningful manual ops work and improves a measurable metric (cycle time, accuracy, cost per claim, attach rate, CSAT)
* You implement an eval harness that catches regressions before production and gives us a reliable “quality score” per workflow
* You establish a scalable architecture pattern for agents (permissions, audit logs, observability, fallbacks) that the team can replicate

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**Tech environment**\
We’re cloud-native and move fast. Expect Python for ML/agents, TypeScript for product surfaces, Postgres for systems of record, event-driven services, and a modern LLM + retrieval stack with strong observability and CI/CD. And AWS+Azure for infra.

**Why this role is special**

* Build an AI-native category-defining company in a massive market
* Direct founder exposure and high leverage: your work will change the trajectory of the company
* Real breadth: growth + underwriting/claims ops + product, in one seat
* Career accelerant: if you perform, your scope and title will grow quickly

**How to Apply**

* **Please ensure your profile is up to date and includes a link to your LinkedIn.**
* **In your application message, share 3 things you’ve built or delivered with the results you achieved in one simple sentence per example (3 sentences total).**

Skills:

Machine Learning, Machine learning