AI Engineer (Agentic Systems) | Gurgaon

About the job

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.

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

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

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