Startups · AI

Senior Applied AI Engineer – Agent Runtime

DataSnipper · New York · Remote

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About DataSnipper

Trusted by all top 100 accounting firms, DataSnipper's AI agents handle audit and finance testing while your team stays in control. Book a demo today. Backed by Index and Insight.

About the role

We are looking for a Senior Applied AI Engineer to join the Agent Runtime team behind Alwin , our new Agentic Automation Platform for audit and finance. Alwin agents run for long times, work through multi-step audit procedures over client evidence, and return finished work papers with every number traced back to source. Humans review and sign off.

What they're looking for

  • 5+ years of software engineering experience with strong, production-grade Python
  • Experience shipping and operating an LLM-powered product in production : you have dealt with hallucinations, latency spikes, tool failures and cost explosions at scale, and can explain what broke and how you fixed it
  • Hands-on experience building agentic systems : control loops, tool selection, planning versus execution, retries and fallbacks, not only prompt-and-parse pipelines
  • Experience with evaluation : you have built datasets, run offline and online experiments, and used the results to make an AI system measurably better
  • Fluency with LLM APIs and agent frameworks across more than one model provider
  • Proficient with AI-assisted engineering and excited about working with coding agents daily
More about this role

We are looking for a Senior Applied AI Engineer to join the Agent Runtime team behind Alwin , our new Agentic Automation Platform for audit and finance. Alwin agents run for long times, work through multi-step audit procedures over client evidence, and return finished work papers with every number traced back to source. Humans review and sign off.

The runtime is the layer every agent depends on: the model access, the base system prompt and context, the tools (document extraction, retrieval, sandboxes, MCP integrations), the orchestration of agents and sub-agents, and the observability and evals that tell us whether an agent is performing to objective standards. You will own the applied AI half of that layer. Product teams build audit-specific agents on top of it; you decide how the base agent reasons, what tools it gets and at what abstraction, how it manages context over long horizons, and how we measure and hill-climb accuracy, speed and cost.

This is a hands-on role in a small team with a large blast radius. Your work goes in front of hundreds of thousands of audit and finance professionals, and the problems are largely open: there is no playbook for production agents in a...

Read the full posting on DataSnipper's site ↗

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