Data Engineer - Document Intelligence

Land Administration Company Inc. (LAC)

Every functioning economy depends on knowing, with certainty, who owns, uses

and holds rights to land. When that information is inaccurate, incomplete or

unavailable, the consequences are serious: disputed titles, stalled transactions,

lost public revenue and eroded trust in government. The Land Administration

Company Inc. (LAC), based in Vancouver, exists to prevent those failures. It

provides mission-critical software and specialist services that help governments

and large institutions modernise and safeguard the systems they use to manage

land, titles, parcels and regulatory processes.

At the heart of LAC's offering is LAVA, its proprietary cloud-native platform. LAVA

serves as the authoritative system of record for land governance, bringing together

land titles, cadastral and spatial data, legal instruments, regulatory compliance

and operational workflows in one secure, highly reliable environment. It supports

the full scope of land registry and cadastral operations, including first registration

and digital survey plan submission. For clients, this means better data quality,

faster approvals, lower administrative risk, real-time decision-making and

complete visibility and control over their land information.

LACI is the research, development and global AI engineering hub for all future LAC

products. Based in Dublin, we work closely with academic and state level

research teams to advance our mission.

About us

LACI is building new products that put applied AI at their core. We’re a small, senior

team that values deep technical ownership, rigorous engineering, and building things

that work reliably in the real world. We’ll share more about what we’re building during

the interview process.

The role

We’re looking for a Data Engineer to turn complex, expert-authored documents into

reliable, retrievable knowledge that our AI systems can reason over and cite with

precision. You’ll own the pipeline from raw documents to well-structured, well-tagged

knowledge: extraction, chunking, retrieval, metadata, and provenance. You’ll work

closely with our Agent Systems and AI Platform & Runtime leads, our Lead Architect,

and domain experts, and your work will directly determine how accurate and

trustworthy our products are. This is a hands-on role with real ownership over a core

part of our system.

What you’ll do

• Design, build, and operate ingestion pipelines that turn PDFs, scanned

documents, and complex tables into clean, structured data.

• Choose and combine the right extraction approaches, including OCR, layout

analysis, table extraction, and LLM-assisted parsing, and know when each one

fails.

• Develop chunking and retrieval strategies suited to long, structured, cross-

referenced documents, and measure their impact on downstream quality.

• Design metadata and tagging schemes that capture a document’s source,

version, scope, and applicability, so the right knowledge is retrieved in the right

context.

• Build fine-grained provenance from source documents through to retrieved

passages, so our AI systems can cite exactly where an answer comes from.

• Create evaluation datasets and metrics for extraction accuracy and retrieval

quality, and use them to make evidence-based improvements.

• Work with domain experts to understand how their documents are structured

and used, and turn that understanding into reliable data models.

• Handle document change over time, including new versions, amendments, and

superseded content, without losing traceability.

• Contribute to our AI-first engineering culture, including how the team uses AI-

assisted development tools, reviews code, and shares best practice.

What you’ll bring

• Solid software or data engineering experience, typically [5+] years, with strong

production experience in Python.

• Hands-on experience extracting structured information from messy real-world

documents, including PDFs, scanned material, and tables.

• Practical experience building retrieval-augmented generation (RAG) or search

systems in production, including chunking, embeddings, and hybrid retrieval.• Experience designing data models, schemas, and metadata for complex or

heterogeneous content.

• A rigorous, evidence-driven approach: you measure extraction and retrieval

quality rather than eyeballing it.

• Care for data quality, lineage, and traceability.

• The ability to work closely with non-technical domain experts and translate their

knowledge into structure.

• Clear communication with both technical and non-technical colleagues.

• Daily, fluent use of AI-assisted development tools in your own engineering work.

Nice to have

• Experience with document AI and OCR tooling, layout-aware models, or vision-

language models for document understanding.

• Experience with vector databases, search engines, and reranking.

• Experience building citation, grounding, or provenance systems for LLM outputs.

• Experience working with long, formal, or highly structured documents such as

standards, specifications, or regulated content.

• Experience with knowledge graphs or entity and relationship extraction.

• Experience in an early-stage or small-team environment.

Why join

• Ownership: You’ll own a core part of our system, and its quality will shape the

quality of everything built on top of it.

• Challenging work: You’ll solve hard document and retrieval problems where

precision really matters.

• AI-first culture: We use AI in what we build and in how we build it.

• Direct access: You’ll work closely with leadership and domain experts in a team

with minimal process.

Benefits

• Salary and equity: A competitive base salary plus a meaningful equity package,

so you share directly in our success.

• Pension: Up to 8% employer-matched pension contribution to help you build for

your future.

• Healthcare: Comprehensive health insurance contribution for peace of mind.

• Learning & Development: A dedicated annual budget for your professional

growth, courses, and certifications.• Home Office Allowance: A one-off budget to help you build an ergonomic and

comfortable remote workspace.

• Bike to Work Scheme: A tax-efficient cycle-to-work scheme to support greener,

healthier commutes.

Interested?

Apply, we’d especially like to hear about a time you turned

difficult documents into reliable, searchable knowledge: what made them hard, how

you measured success, and what you learned along the way.

The Land Administration Company Ireland is an equal opportunity employer. Applicants

are considered without regard to race, colour, religion, age, gender, marital status,

sexual orientation or identity.