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

DataSnipper

Amsterdam

Hybrid

EUR 110,000 - 160,000

Full time

4 days ago

Be an early applicant

Application generator

Stand out for this role — generate a tailored resume and cover letter in about a minute.

Get past ATS filters

Benefits offered by this job

Hybrid work (Amsterdam-based)

Pension plan

Stock participation plan

International team and environment

Daily lunch

Mental health support (OpenUp)

Social events and team activities

Job summary

DataSnipper, based in Amsterdam with a global footprint, seeks a Senior Applied AI Engineer to own the base agent runtime layer of Alwin, our end-to-end audit and finance workflow platform. You will shape model access, context, and tool usage across multi-turn conversations, balancing accuracy, latency and cost while collaborating with product teams and auditors.

You will lead the hands-on development of agentic systems, evaluating models, designing robust pipelines, and shipping

Qualifications

5+ years of software engineering experience with production-grade Python.
Experience shipping and operating an LLM-powered product in production, handling latency, costs and failures.
Hands-on building agentic systems: control loops, tool selection, planning vs execution.
Experience with evaluation: datasets, offline/online experiments for measurable improvements.
Fluency with LLM APIs and agent frameworks across multiple providers.
Proficient in AI-assisted engineering and coding agents daily.
Strong software architecture and system design fundamentals with reliable delivery.
Excellent communication with auditors and product partners to define correctness.

Responsibilities

Own and evolve the base agents: prompts, context, memory and state for long-running tasks.
Design integration with the tool layer: document extraction, sandboxes and MCPs.
Implement agentic patterns: sub-agent composition, planning modes, human-in-the-loop gates.
Ship end-to-end: from prototype with domain experts to production on Alwin.
Define automatic processes to improve agents over time.
Keep costs under control with cost-efficient AI-centric workflows.
Instrument agent behavior with observability; drive improvements.

Skills

Python

LLM APIs

Agent frameworks

Software architecture

Cross-functional collaboration

Auditor liaison

Cost management

Tools

Document extraction

Retrieval pipelines

Sandboxed code execution

MCP integrations

Observability stack

Job description

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