Lead design and delivery of an AI Data Readiness platform: build unstructured data pipelines, vector/graph indexing, embeddings, ingestion standards, data contracts, automated validation and governance, SLAs/SLOs, cloud-native infrastructure optimization, and lead a team to support enterprise AI readiness.
The Company and Our Mission
Zartis is a global AI transformation and technology consulting partner where talented engineers and technologists work on cutting-edge innovation. We partner with ambitious organizations to design, build, and scale technology solutions that deliver real impact.
Our teams bring deep expertise in AI-driven platforms, secure API architectures, and cloud-native engineering. You will work on meaningful projects that accelerate the adoption of advanced technologies — from strategy and discovery through to full product delivery — helping turn complex challenges into measurable outcomes.
With engineering hubs across EMEA and LATAM, and long-term partnerships in financial services, healthcare and life sciences, and energy and climate, we offer opportunities to work on projects that truly matter. Here, you will not just build technology — you will drive business impact and grow your career alongside industry leaders.
We are looking for a Director of AI Data Platform to help shape the foundational data infrastructure powering next-generation AI solutions.
The Project:
You will lead the development of a core AI Data Readiness platform that transforms fragmented enterprise data into clean, governed, and discoverable assets ready for AI consumption.
This is a strategic, high-impact role where you will serve as the primary architect of the data-to-AI lifecycle, ensuring both structured and unstructured data assets are AI-ready from day one. You will oversee the end-to-end processing lifecycle—including ingestion, parsing, and enrichment—to guarantee the accuracy and governance of all published assets.
What You Will Do:
Unstructured Data & Indexing Layer (Core Ownership)
- Lead the development of pipelines for unstructured data sources, including document parsing, OCR, and text normalization
- Own the indexing strategy beyond traditional SQL storage, architecting structures optimized for LLM consumption
- Design and oversee semantic chunking, embedding strategies, and hybrid search across Vector and Graph architectures
- Establish metadata extraction protocols to ensure high-fidelity knowledge capture for internal and client use
Data Ingestion & Standardization
- Define standards and tooling for ingesting data from client systems (batch and streaming)
- Build reusable connectors and canonical schemas to eliminate redundant, project-specific custom work
Data Quality & Governance
- Implement Data Contracts with upstream systems and downstream consumers to guarantee data integrity
- Establish automated validation gates for completeness, consistency, anomaly detection, and drift monitoring
- Ensure “Governance by Default,” including PII detection/redaction, encryption, and audit trails for GDPR and AI Act compliance
Operational Excellence
- Define and maintain SLAs/SLOs for ingestion pipelines (freshness, uptime, failure rates)
- Optimize cloud-native infrastructure costs and implement robust monitoring and incident playbooks
Strategic Stakeholder Management (Dual Focus)
- Architect and manage the data infrastructure powering operational efficiency and internal IP
- Serve as the lead architect for enterprise customers, designing high-performance AI data readiness strategies aligned with business goals
Leadership & Delivery Enablement
- Lead and scale a specialized team of data and platform engineers
- Provide documentation, standards, and self-service capabilities for cross-functional teams
What You Will Bring:
- 10+ years of experience in data-heavy engineering or platform environments
- 2+ years leading platform or data engineering teams
- Proven track record of delivering reusable internal data capabilities rather than one-off pipelines
- Strong software engineering foundation with a platform mindset
- Advanced Database Expertise
- Mandatory experience with Vector Databases such as Pinecone, Milvus, or Weaviate
- Proven ability to structure enterprise data for LLMs, including chunking, enrichment, embedding, and relationship mappingDeep expertise in SQL, ETL/ELT, and data modeling
- Strong experience with parsing, OCR, metadata enrichment, and unstructured pipelines
- Experience with cloud-native architectures (Containers, IaC)
- Strong operational mindset across monitoring, alerting, and cost management
- Strong proficiency in LLM concepts and AI-ready data design
Nice to Have:
- Knowledge graph design and hybrid retrieval systems
- Experience advising enterprise customers on AI transformation
- Background in highly regulated industries (finance, healthcare, energy)
What We Offer:
- 100% Remote Work
- WFH allowance: Monthly financial support for remote working
- Career Growth: Established career development program with 360º feedback to support progression
- Training: Weekly allocated time for learning, including online courses (Pluralsight, Educative.io), English classes, books, conferences, and events
- Mentoring Program: Opportunities to mentor, be mentored, or both
- Zartis Wellbeing Hub (Kara Connect): Access to mental health professionals, nutritionists, physiotherapists, fitness coaches, and wellbeing webinars
- Multicultural working environment: Tech events, webinars, online team-building activities, and company celebrations
Top Skills
Containers
Elt
Embeddings
ETL
Graph Architectures
Iac
Llms
Milvus
Ocr
Pinecone
SQL
Vector Databases
Weaviate
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