Zinnia
life and annuities
SeniorAIEngineer
“Senior AI Engineer at Zinnia. Skills: LLM-based systems, agentic systems, Retrieval-Augmented Generation (RAG) systems, evaluation frameworks for AI systems, Python, cloud-native distributed systems. contribute to agentic transaction processing systems that embed AI directly into operational workflows. support the development of a unified intelligent agent network”
What You'll Achieve.
Agentic components are reused across workflows rather than rebuilt for each use case; AI-driven automation measurably increases straight-through processing and reduces manual intervention; Model and agent updates are evaluated against shared benchmarks before release; A experiments demonstrate statistically significant improvements prior to scale; Regressions are detected automatically; Performance, cost, and risk are continuously monitored
Industry & Context.
simplifies the experience of buying, selling, and administering insurance products; enables more people to protect their financial futures
What They're Looking For.
Must Have
at least five years of experience building production software systems, meaningful experience deploying LLM-based or agentic systems in real-world environments, at least 2 years of experience implementing Retrieval-Augmented Generation (RAG) systems, understand the tradeoffs in chunking, embedding strategies, hybrid retrieval, re-ranking, and grounding evaluation, hands-on background with MCP (Model Context Protocol) Architecture/Servers, knowledge Graphs, 1 year of experience building or significantly contributing to multi-step agentic workflows involving tool execution, planning, orchestration, or transactional automation, at least 2 years of experience designing evaluation frameworks for AI systems, comfortable with statistical testing, experiment design, and interpreting noisy performance signals, understand the limitations of automated grading and the risks of benchmark overfitting, experience running A experiments in production systems and defining decision thresholds grounded in measurable impact, highly proficient in Python, comfortable building cloud-native distributed systems with observability and versioning practices, Python (FastAPI, Pydantic, async) or TypeScript/Node (Express/Fastify/Next API routes), testing (pytest/jest), Git/PR hygiene, CI/CD, Implement LLM evaluation & guardrails: prompt/unit evals, Ragas, Langfuse, LangSmith, A tests, hallucination & safety checks, feedback loops, understand the governance and risk implications of deploying AI systems in regulated environments, can design for auditability and control from day one
Nice to Have
Knowledge Graphs
What You'll Do.
contribute to agentic transaction processing systems that embed AI directly into operational workflows
support the development of a unified intelligent agent network
build the experimentation backbone that ensures every AI capability is measurable
design offline evaluation pipelines
maintain regression test suites for non-deterministic systems
implement backtesting frameworks to compare models
and orchestration strategies
design and execute controlled A tests in production
define statistical guardrails for AI/ML model promotion
implement continuous monitoring systems that track accuracy
help establish reusable components and standards that enable teams to build on the platform without duplicating logic or fragmenting architecture
How You'll Work.
Team & Collaboration
team up; collaborate with smart, creative professionals
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