Senior Technical Product Manager – Document Intelligence

Datasnipper; Amsterdam; On-site; EUR 70,000 - 110,000; Full time

Job summary

Jobtailor seeks a product leader to own the document intelligence pipeline and set the technical roadmap for ingestion, parsing, extraction, classification, and summarization of unstructured documents. You will translate customer and agent-team needs into APIs, workflows, and internal platforms, ensuring fast, reliable storage and retrieval while designing evals to measure quality at scale.

Collaborate with engineering as a technical peer, balancing build vs buy and optimizing accuracy,

Qualifications

• 4+ years in product management, with 2+ years on technical/platform/data products
• Strong grounding in document or data processing: extraction, classification, parsing/OCR, summarization, or NLP; grasp of LLM approaches
• Background in distributed systems, data infrastructure, or pipelines
• Treats non-functional requirements as a first-class product surface: accuracy, throughput, latency, cost, reliability, observability
• Hands-on experience creating and running evals or quality-measurement methods for AI/ML or data systems at scale
• Fluency in model and tooling operations: model/technique selection, build-vs-buy, cost/performance/accuracy trade-offs
• Can write technical specs that engineers review for feasibility and prototype with code

Responsibilities

• Own the document intelligence pipeline.
• Technical direction for ingestion, parsing, extraction, classification, and summarization of unstructured documents.
• Own the product strategy for the document intelligence platform, translating needs into APIs, workflows, and internal platforms.
• Manage storage, indexing, and serving to enable fast, reliable retrieval.
• Create and run evals to measure and improve extraction, classification, and summarization quality at scale.
• Decide on models and build-vs-buy trade-offs; optimize cost, performance, and accuracy.
• Partner with engineering as a technical peer to drive delivery.

Skills

Product management

APIs

Data pipelines

NLP/ML

LLM tools

Distributed systems

Evaluation methods

Model operations

Build-vs-buy

Technical specs

Job description

Responsibilities

• Own the document intelligence pipeline.
• Technical direction for how we ingest, parse, extract, classify, and summarize unstructured documents — the tooling that converts raw documents into structured, queryable data.
• Own the product strategy for the document intelligence platform, translating customer and agent-team needs into clear requirements and the tooling roadmap (APIs, workflows, and internal platforms) that makes those needs shippable and maintainable.
• How processed documents and their derived data are stored, indexed, and served — so agentic flows can retrieve the right information quickly and reliably.
• Own the creation and running of the evals that measure and improve extraction, classification, and summarization quality at scale — defining the quality bar each capability meets before it ships.
• Decisions on models, extraction techniques, and build‑vs‑buy across the document‑processing stack; cost/performance/accuracy trade‑offs; staying current as document‑AI techniques evolve.
• Engineering is your primary partner — you operate as a technical peer to engineering managers and tech leads.

Qualifications

• 4+ years in product management, with 2+ years on technical/platform/data products (APIs, infrastructure, data pipelines, ML systems, or developer tools)
• Strong grounding in document or data processing: extraction, classification, parsing/OCR, summarization, or other intelligent‑document‑processing / NLP techniques — with enough grasp of LLM‑based approaches to judge when they’re the right tool
• Background in distributed systems, data infrastructure, or pipelines — you understand how processing systems behave under scale, volume, and variety
• Treats non‑functional requirements as a first‑class product surface: accuracy, throughput, latency, cost, reliability, observability
• Hands‑on experience creating and running evals or quality‑measurement methods for AI/ML or data systems at scale
• Fluency in model and tooling operations: model/technique selection, build‑vs‑buy, cost/performance/accuracy trade‑offs
• Can write technical specs that engineers review for feasibility (not correctness) and prototype with code to validate hypotheses; comfortable with architecture trade‑offs (accuracy vs. cost, coverage vs. latency)