Asks for

The ad says“Deep understanding of LLM-based systems and agent architectures: prompting, tool use, planning, multi-step agent loops, context and memory, multi-agent systems - with enough grasp of retrieval/vector search to judge when it's the right tool”
The ad says“4+ years in product management, with 2+ years on technical/platform products (APIs, infrastructure, ML systems, or developer tools)”
The ad says“Deep understanding of LLM-based systems and agent architectures: prompting, tool use, planning, multi-step agent loops, context and memory, multi-agent systems - with enough grasp of retrieval/vector search to judge when it's the right tool”
The ad says“4+ years in product management, with 2+ years on technical/platform products (APIs, infrastructure, ML systems, or developer tools)”
The ad says“Background in distributed systems or infrastructure - you understand how runtimes behave under scale and how a change ripples across the stack”
The ad says“4+ years in product management, with 2+ years on technical/platform products (APIs, infrastructure, ML systems, or developer tools)”
The ad says“Deep understanding of LLM-based systems and agent architectures: prompting, tool use, planning, multi-step agent loops, context and memory, multi-agent systems - with enough grasp of retrieval/vector search to judge when it's the right tool”
The ad says“4+ years in product management, with 2+ years on technical/platform products (APIs, infrastructure, ML systems, or developer tools)”
The ad says“Fluency in model operations: model selection and swaps, provider trade-offs, token cost/performance optimization”
The ad says“Deep understanding of LLM-based systems and agent architectures: prompting, tool use, planning, multi-step agent loops, context and memory, multi-agent systems - with enough grasp of retrieval/vector search to judge when it's the right tool”
The ad says“Deep understanding of LLM-based systems and agent architectures: prompting, tool use, planning, multi-step agent loops, context and memory, multi-agent systems - with enough grasp of retrieval/vector search to judge when it's the right tool”
The ad says“Deep understanding of LLM-based systems and agent architectures: prompting, tool use, planning, multi-step agent loops, context and memory, multi-agent systems - with enough grasp of retrieval/vector search to judge when it's the right tool”
The ad says“4+ years in product management, with 2+ years on technical/platform products (APIs, infrastructure, ML systems, or developer tools)”
The ad says“Deep understanding of LLM-based systems and agent architectures: prompting, tool use, planning, multi-step agent loops, context and memory, multi-agent systems - with enough grasp of retrieval/vector search to judge when it's the right tool”
The ad says“Can write technical specs that engineers review for feasibility (not correctness) and prototype with code to validate hypotheses; comfortable with architecture trade-offs (latency vs. accuracy, cost vs. capability)”
The ad says“Deep understanding of LLM-based systems and agent architectures: prompting, tool use, planning, multi-step agent loops, context and memory, multi-agent systems - with enough grasp of retrieval/vector search to judge when it's the right tool”
The ad says“Can write technical specs that engineers review for feasibility (not correctness) and prototype with code to validate hypotheses; comfortable with architecture trade-offs (latency vs. accuracy, cost vs. capability)”
The ad says“Fluency in model operations: model selection and swaps, provider trade-offs, token cost/performance optimization”
The ad says“Deep understanding of LLM-based systems and agent architectures: prompting, tool use, planning, multi-step agent loops, context and memory, multi-agent systems - with enough grasp of retrieval/vector search to judge when it's the right tool”

We're looking for a Technical PM to own the agent runtime and the platform that powers our agentic products — and the non-functional requirements that make those agents accurate, fast, reliable, and cost-effective at scale.

You'll own how well the platform runs, not the end-user features on top of it. You'll define the runtime architecture, own the product evals that prove our agents stay accurate as we scale and upgrade models, and set the bar for latency, reliability, cost, and observability across our agent runtimes.

This is an engineering-facing role — but it's a product role at heart. You translate what customers and the business actually need into the architecture that delivers it: product judgment expressed through technical decisions. You'll spend your time in architecture discussions and eval design alongside ML and backend engineers, not in design reviews. When you work with go-to-market, it's to turn runtime scale, stability, and reliability into a story the market trusts. And you'll track how the frontier of agentic engineering is moving — keeping us ahead of it and being a visible voice on it inside and outside the company.

If you've never written a prompt chain, debugged a pipeline, reasoned about token cost, or designed an eval, this isn't the right fit.

About DataSnipper

Audit and finance are still massively manual and we are changing that. DataSnipper is a $1B, bootstrapped unicorn with 600,000+ users across 180+ countries, already embedded in the daily workflows of top audit and accounting firms.

Now, we are taking things further with our Excel Agent, bringing AI directly into where the work actually happens. Unlike generic AI tools, we do not sit on the sidelines. Our AI operates inside Excel, with access to real documents and audit evidence, meaning it does not just generate answers, it does the work, with full traceability.

We are not just applying AI, we are redefining how audit gets done. If you want to build something category-defining at scale, this is the place.

What you'll own

Agent runtime architecture. Technical direction for our agent runtimes: agent orchestration and multi-step loops, context and memory management, retrieval strategy, tool execution and orchestration, model routing and fallbacks, guardrails, caching, streaming, and concurrency.
Non-functional requirements of the platform. How well the platform runs - latency, throughput, reliability, graceful degradation, scalability under concurrent agent load, token-cost economics, security, and observability/telemetry. These are your primary success metrics, not feature counts.
Product evals. Own the creation and running of the evals that prove our agents are accurate and reliable - defining the quality bar every agent meets before it ships, and the measures that let us improve and upgrade models with confidence. You work hand in hand with our LLM Ops team, who own the eval infrastructure; you own the evals themselves and what they tell us.
Model strategy. Model selection, swaps, and provider decisions; cost/performance trade-offs; staying current as frontier models move.
Market intelligence & thought leadership. Track how the field is building agentic systems. Own a point of view on what differentiates us and where the runtime must go to stay ahead - and evangelize it both internally and externally (talks, writing, customer and industry conversations).
Cross-functional partnership. Engineering is your primary partner - you operate as a technical peer to engineering managers and tech leads, not just a prioritization partner. You translate experience-team needs into runtime capability, and partner with go-to-market on scale, stability, and reliability.

Who you are

A tinkerer. You build to understand - prototypes, prompt chains, quick experiments. You'd rather try it than theorize about it.
A product thinker in an engineer's seat. You don't need to be an auditor, but you love representing the customer and the business problem - and turning that understanding into strong architecture. The technical depth is in service of product value, not an end in itself.
Highly autonomous. You operate with little direction: you find the problems that matter, set the direction, and drive them without waiting to be told.
Entrepreneurial. You treat your area like your own company - scrappy, outcome-obsessed, and comfortable making the call under ambiguity.

What you'll bring

Must-have:

4+ years in product management, with 2+ years on technical/platform products (APIs, infrastructure, ML systems, or developer tools)
Deep understanding of LLM-based systems and agent architectures: prompting, tool use, planning, multi-step agent loops, context and memory, multi-agent systems - with enough grasp of retrieval/vector search to judge when it's the right tool
Background in distributed systems or infrastructure - you understand how runtimes behave under scale and how a change ripples across the stack
Treats non-functional requirements as a first-class product surface: latency, reliability, scalability, cost, observability
Hands-on experience creating and running evals that measure and improve agent accuracy and reliability at scale
Fluency in model operations: model selection and swaps, provider trade-offs, token cost/performance optimization
Can write technical specs that engineers review for feasibility (not correctness) and prototype with code to validate hypotheses; comfortable with architecture trade-offs (latency vs. accuracy, cost vs. capability)

Strong-to-have:

Former software engineer, ML engineer, or data scientist who moved into product
A public point of view on AI/agents - writing, talks, OSS - or the appetite to build one
Experience in audit, accounting, or financial services (domain context for what the agents do)

What We Offer

Be part of one of the fastest-growing, profitable unicorn scale-ups in the Netherlands with a global impact.
Equity (Stock Appreciation Rights) to share in the company’s success and growth.
Pension plan with a 6% contribution on top of your base salary.
28 vacation days per year (full-time) to support your work-life balance.
Hybrid work model with at least 3 days onsite in our dynamic Amsterdam office.
Daily, freshly prepared lunches by our in-house chef to keep you energised.
NS business card for easy commuting to the office.
A structured onboarding programme designed to set you up for success, including dedicated time to learn our product and customers before you hit the ground running.
Access to continuous learning and development initiatives to grow your skills.
Engage with a vibrant international team spread across seven global offices.
Company-wide events like DataSnipper GO, where global teams come together.
Access to OpenUp, a mental health and wellness platform supporting your wellbeing.

Recruitment steps

Recruiter screen
Hiring Manager interview
Assessment interview
Final interview with Engineering leadership