5 publicaciones de empleo destacadas de databricks data engineer en madrid, md (¡Están contratando!)

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Databricks Resident Solutions Architect (m/f/*)
Ultra Tendency —Madrid, Madrid provincia
We are expanding our Databricks practice and are looking for an experienced Databricks Resident Solutions Architect (RSA) to work closely with our clients and…
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Senior AI Engineer

Control Risks -
Madrid, Madrid provincia

Solicitud rápida

Detalles del empleo

Jornada completa
Hace 8 días

Cualificaciones

Optimización del rendimiento
Derecho
Inglés
Investigación
.NET
Máster
Neo4j
GitHub
APIs
Cloud
LMS
Inteligencia artificial
Python

Descripción completa del empleo

As Senior AI Engineer you will own the AI and data layer of the platform. You will design and implement the knowledge graph architecture, build agentic pipelines that enrich entity data through AI web research, and develop the graph interrogation layer that translates natural language compliance questions into structured graph queries.

You will work closely with the CTO and a small AI team including a data engineer and a junior AI engineer. You will have significant technical autonomy and direct influence over how the product evolves.

Please submit yoru CV in English.

Requirements

What You’ll Work On

Knowledge graph architecture and implementation

Own and evolve the platform’s knowledge graph data model — entities (Company, Person, Community, Episodic), relationships, and temporal attributes
Implement and extend custom entity and relationship extraction pipelines using LMs, with structured output validation and confidence scoring
Use Graphiti (Zep AI’s temporal knowledge graph framework) as the memory layer, managing episode ingestion, entity resolution, and graph updates
Design and enforce graph schema standards, ensuring consistency across data sources and ingestion pipelines
Build evaluation frameworks to measure extraction quality, entity disambiguation accuracy, and graph coverage

Agentic AI pipelines

Design and implement multi-step agentic workflows that orchestrate LLM calls, web search, database lookups, and graph writes
Integrate AI web search (Tavily, Perplexity, or similar) as a tool within agentic pipelines for real-time entity enrichment
Build retrieval-augmented generation (RAG) pipelines over the knowledge graph, translating compliance queries into Cypher and natural language answers
Implement the graph interrogation layer — a conversational interface for compliance analysts to query the knowledge graph without writing Cypher
Manage LLM API integrations (Anthropic Claude, Gemini) including prompt engineering, structured outputs, and cost/latency optimisation

Data pipelines and integrations

Build and maintain Python-based data ingestion pipelines reading from Databricks, connecting to external APIs (sanctions lists, corporate registries, Polixis, Vantage), and writing to Neo4j
Implement NER (named entity recognition) and entity disambiguation logic for extracting structured facts from unstructured compliance documents
Develop topic modelling pipelines to classify extracted facts as knowledge graph relationship attributes
Integrate with the .NET backend via well-defined HTTP contracts, ensuring the AI layer is independently deployable and testable

Research and technical leadership

Stay current with the fast-moving agentic AI and knowledge graph literature — evaluate new frameworks, models, and techniques for production applicability
Contribute to architectural decisions with evidence: benchmarks, prototypes, and documented trade-offs
Maintain Claude Code context files (CLAUDE.md, SKILLS.md) for the AI codebase, enabling AI-augmented development across the team
Write clear technical documentation in Notion covering design decisions, data models, and operational runbooks.

What We’re Looking For

Must-have

3+ years of professional experience in AI/ML engineering, with at least 1 year working on knowledge graphs or graph-based data systems in production
Hands-on experience with Neo4j — data modelling, Cypher query writing, schema design, and performance tuning
Deep familiarity with LLM APIs (OpenAI, Anthropic, or Gemini) — prompt engineering, structured outputs, function/tool calling, and cost management
Understanding the trade-offs between LLMs and smaller fine-tuned models (Hugging Face Transformers, sentence-transformers) — knowing when a purpose-built model outperforms a prompted general one on cost, latency, and accuracy
Experience building agentic pipelines — multi-step LLM workflows with tool use, memory, and state management
Python proficiency — you write clean, testable Python and understand async patterns for I/O-bound AI workloads
Experience with RAG architectures — vector search, hybrid retrieval, and integrating retrieval into LLM-powered applications
Ability to read and apply AI research papers — you can take a paper on narrative extraction or entity disambiguation and turn it into a working prototype
Strong English communication skills — written and verbal

Strong advantage

Direct experience or knowledge with Graphiti (Zep AI) or other temporal knowledge graph frameworks
Familiarity with entity resolution and disambiguation techniques at scale
Experience with NER pipelines, topic modelling, or information extraction from compliance or financial documents
Knowledge of the third-party risk, sanctions screening, or KYC/AML domain
Experience with Databricks, Delta Lake, or similar data lakehouse platforms
Exposure to GCP (Vertex AI, Cloud Run, Pub/Sub, Cloud Storage)
Experience designing evaluation frameworks for LLM-generated structured outputs
Prior work in a RegTech, FinTech, or compliance-adjacent environment

What Good Looks Like in This Role

We are direct about what we expect. The person who thrives here will:

Treat the knowledge graph data model as the product’s most important artefact — they think carefully about schema decisions and document them
Read the literature. When evaluating a new approach to entity disambiguation or graph memory, they check what’s been published, not just what’s on GitHub
Write evaluation harnesses before scaling pipelines. They know that a pipeline that produces plausible-looking output is not the same as one that produces correct output
Communicate blockers and trade-offs early. Technical uncertainty is expected — silence is not

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