Data Engineer with AI capabilities

About the role

This role owns the architecture and
delivery of data and AI solutions that make enterprise data trustworthy and
usable by AI. It combines solution architecture, hands-on data and AI
engineering, and leadership of a multi-member delivery team.

Key responsibilities

• Architecture: Own the end-to-end
  architecture of data and AI solutions, including the UI/UX and backend services
  of the applications built on them.
  
• Standards: Define data product and data
  contract standards aligned with data mesh principles, adopting open standards
  such as ODPS and ODCS.
  
• AI agents: Build AI agents and LLM
  applications using open-source frameworks, protocols and patterns such as
  LangGraph, LangChain, the Agent2Agent (A2A) protocol, Model Context Protocol
  (MCP) servers and retrieval-augmented generation (RAG).
  
• Agent engineering: Deliver reliable
  structured output, tune prompts, measure agent quality, and deploy and run
  agents at scale using DataRobot.
  
• AI readiness: Design explainable
  readiness frameworks with clear dimensions, rules, scoring and remediation
  guidance.
  
• Semantic layer: Understand and build
  ontologies, knowledge graphs, glossaries and taxonomies aligned to W3C DCAT and
  PROV-O.
  
• Data engineering: Build data models,
  pipelines and governance integrations, and enforce quality and lineage
  standards.
  
• Performance: Diagnose and fix
  performance and scalability bottlenecks across data and AI pipelines within
  agreed SLAs.
  
• Leadership: Lead and mentor the team,
  plan capacity, review designs and code, and own delivery to production.
  
• Stakeholders: Turn requirements into an
  architecture and a backlog, and present points of view and designs to clients.
  

Skills

• AI-led development: UI/UX development, backend engineering, agent development and
  troubleshooting
  
• Data engineering: Python, SQL,
  PostgreSQL, SQL Server, semi-structured data, data modeling, pipelines, GitLab,
  CI/CD
  
• AI and LLM: Agent design, prompt
  engineering, structured outputs and LLM evaluation
  
• Data governance: Data mesh architecture
  
• Semantic technologies: Ontologies,
  knowledge graphs, RDF, DCAT, PROV-O, taxonomies and glossaries
  
• Integration and security: Secrets
  management (for example, HashiCorp Vault), security scanning (Snyk), code
  quality (SonarQube) and observability (Dynatrace)
  
• Leadership: Team leadership, delivery
  management, capacity planning, stakeholder management and executive
  presentations
  

Qualifications

• 14 years of experience in data
  engineering, including hands-on work building AI agents and LLM applications.
  
• Proven record of architecting
  enterprise data solutions and leading teams to production.
  
• Degree in computer science,
  engineering or a related field, or equivalent experience.