Senior Technical Product Manager (Agent Runtime)

Datasnipper; Netherlands; On-site; EUR 90,000 - 130,000; Full time

Job summary

Datasnipper is seeking a Technical Product Manager to own the agent runtime and platform that powers its agentic products. You will define runtime architecture, lead product evaluations to prove accuracy at scale, and set latency, reliability, cost, and observability standards across runtimes.

This is an engineering-facing role with a product core: translating customer needs into technical decisions, and shaping the future of our agentic platform through architecture reviews and eval design.

Qualifications

• 4+ years in product management, with 2+ years on technical/platform products (APIs, infrastructure, ML systems, or developer tools)
• Background in distributed systems or infrastructure
• Fluency in model operations: model selection and swaps, provider trade-offs
• Former software engineer, ML engineer, or data scientist who moved into product
• Public point of view on AI/agents – talks, OSS, or willingness to build one

Responsibilities

• Own agent runtime architecture and orchestration of multi-step loops
• Define runtime architecture and evaluation design for accuracy and reliability
• Translate customer and business needs into architecture and product decisions
• Collaborate with ML and backend engineers on runtime discussions and evals
• Partner with go-to-market to communicate scale, stability, and reliability

Skills

Product management

Technical leadership

Architecture discussions

Eval design

Prompt engineering

LLM Ops liaison

Distributed systems

Model operations

Tools

APIs

Infrastructure

ML systems

Developer tools

Job description

• 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
• 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

Highly autonomous. You operate with little direction: you find the problems that matter, set the direction, and drive them without waiting to be toldEntrepreneurial. You treat your area like your own company - scrappy, outcome-obsessed, and comfortable making the call under ambiguityA tinkerer. You build to understand - prototypes, prompt chains, quick experiments. You’d rather try it than theorize about itA 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 itselfTreats non-functional requirements as a first-class product surface: latency, reliability, scalability, cost, observabilityHands-on experience creating and running evals that measure and improve agent accuracy and reliability at scaleBackground in distributed systems or infrastructure - you understand how runtimes behave under scale and how a change ripples across the stackDeep 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 toolCan 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)4+ years in product management, with 2+ years on technical/platform products (APIs, infrastructure, ML systems, or developer tools)Fluency in model operations: model selection and swaps, provider trade-offs, token cost/performance optimizationExperience in audit, accounting, or financial services (domain context for what the agents do)A public point of view on AI/agents - writing, talks, OSS - or the appetite to build oneFormer software engineer, ML engineer, or data scientist who moved into product