Senior Applied AI Engineer – Agent Runtime (Alwin by DataSnipper)

Datasnipper; New York (NY); Presencial; USD 150 000 - 210 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Hybrid work model

Competitive salary

Medical and dental coverage

401K match

Paid parental leave

Office in New York City

Resumo da oferta

DataSnipper is building Alwin, an Agentic Automation Platform for audit and finance. This Senior Applied AI Engineer will shape the runtime layer, enabling long-running agents, tool use, and cost-aware execution across audits.

You’ll work with cross-functional teams and influence model routing, evaluation, and observability in production. The role sits in a small team with a wide reach and hundreds of thousands of audit professionals relying on our outputs.

Qualificações

• 5+ years of software engineering experience in production systems.
• Hands-on with shipping an LLM-powered product and managing costs.
• Proven ability to design agentic, multi-turn systems and evaluate them.

Responsabilidades

• Own the base agents: system prompts, context engineering, memory and state management.
• Design tool layer usage and abstraction for documents, retrieval, sandboxes and MCPs.
• Implement agentic patterns: sub-agent composition, planning modes, human-in-the-loop gates.
• Ship end-to-end from prototype to production on Alwin.
• Improve agent performance and control costs over time.
• Collaborate with product and audit teams to turn needs into runtime capabilities.

Conhecimentos

Five years experience

Python

LLM production experience

Agentic systems

Evaluation

LLM APIs

Software architecture

Cross-team communication

Ferramentas

Agent frameworks

RAG pipelines

Document AI

Sandboxed code execution

MCP integrations

Descrição da oferta de emprego

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 regulated domain, so you will help write it.

About DataSnipper

DataSnipper is the Agentic Automation Platform for audit and finance. Known worldwide for our Excel add-in, we are now building Alwin by DataSnipper: purpose-built agents that execute audit and finance workflows end-to-end, with every output traceable back to source evidence and a human signing off. Headquartered in Amsterdam with offices in New York, Tokyo, Kuala Lumpur and Sydney, we are used by hundreds of thousands of professionals at the world's largest firms. Our mission: automate the mundane, unlock the meaningful.

What You Will Do

Agent Engineering

• Own the base agents: system prompt, context engineering, memory and state management for tasks that span many turns and hours of execution
• Design how agents use the tool layer, including document extraction, retrieval, code and file sandboxes, and MCP integrations, choosing the right level of abstraction so agents handle edge cases without wasting effort on mechanical steps
• Implement agentic patterns such as sub-agent composition, planning modes, human-in-the-loop gates, and model routing across multiple providers
• Ship end-to-end: from prototype with our audit domain experts, through evaluation, to production on Alwin
• Define and build automatic processes that improve the agent continuously over time
• Keep costs under control and implement cost-efficient approaches to AI-centric workflows

Evaluation & Quality

• Extract signal from long agent trajectories: attribute outcomes to specific reasoning steps and tool calls, classify failure modes, and turn them into fixes
• Hill-climb accuracy, latency and token cost, and make the trade-offs explicit for the teams building on the runtime

Reliability & Collaboration

• Instrument agent behaviour with our observability stack and use production traces to drive improvements
• Partner with Agent Experience teams, Document Intelligence and the AI Platform team to turn their needs into runtime capabilities that are self-service rather than a request queue
• Stay on the frontier: evaluate new models, techniques and agent patterns, and bring the ones that hold up into production

What You Will Bring

Must-Have

• 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
• Strong fundamentals in software architecture and system design, and a track record of reliable, well-tested delivery
• Excellent communication; you can work directly with auditors and product partners to define what "correct" means

Nice-to-Have

• Experience with Durable workflow engines or long-running background task systems
• Experience with RAG and retrieval pipelines over large, messy document corpora
• Document AI: VLMs, OCR, structured extraction and their metrics
• Sandboxed code execution, MCP, or multi-agent orchestration in production
• Domain experience in audit, accounting or fintech
• Familiarity with OWASP GenAI security practices and working in a regulated, privacy-sensitive environment

What We Expect

• Ownership: You own work end-to-end, anticipate issues, and ensure high-quality delivery with minimal support. We expect you to influence the technical direction of the team.
• Growth Mindset: You encourage open feedback exchange and provide clear, balanced feedback that helps others grow
• Collaboration: You build strong cross-functional relationships and influence peers through expertise, data, and empathy
• Adaptability: You navigate ambiguity calmly, model positive behavior, and help peers adjust through clear communication
• Judgment: You exercise sound judgment in ambiguous situations, balance speed and accuracy, and adjust priorities proactively

What we offer

• Excellent salary stock participation plan
• Flexible paid time off
• Comprehensive medical and dental coverage
• 401K match
• Paid parental leave
• Company-sponsored lunch
• Hybrid mode of work (at least 3 days onsite in our New York City office)
• Being part of one of the fastest-growing scale-ups in the Netherlands
• Make an impact by disrupting the finance industry with us
• A flexible and growing organization with lots of opportunities to learn and develop
• International working environment, with a team of friendly and driven colleagues
• Access to OpenUp and Talkspace, the mental health and wellness platform
• Multiple social activities for team building

Recruitment steps

• Recruiter screen
• Hiring Manager interview
• Peer programming session
• System design interview
• Final interviews with Engineering leadership