Forward Deployed Engineer - Data Management

Systems Limited; Malaysia; On-site; MYR 180,000 - 300,000; Full time

Job summary

Systems Ltd is seeking a Forward Deployed Engineer - Data Management to make enterprise data usable by AI—build AI-ready data products, knowledge graphs, and retrieval infrastructure that every practice depends on.

You will own vector and retrieval infrastructure, run data quality assessments for AI/ML, and collaborate with GenAI Engineers, Data Scientists, and AI Architects to expose curated data as reusable assets.

Qualifications

• 5-10+ yrs data engineering, with 2+ yrs building AI-ready data products specifically
• Strong in knowledge graph technologies (Neo4j, RDF/SPARQL, or similar) and semantic/ontology modeling
• Experience with vector/retrieval infrastructure (embeddings, ANN indexes, hybrid search)
• Solid data pipeline engineering (Spark, dbt, Airflow, or similar) and data quality frameworks
• Familiarity with enterprise data governance and lineage tooling
• Can explain the difference between BI-grade and AI-grade data quality to a non-technical stakeholder
• Collaborates closely with GenAI Engineers, Data Scientists, and AI Architects as a shared upstream dependency
• Prioritizes competing requests from multiple practices fairly and transparently
• Documents clearly enough that other teams can self-serve without hand-holding
• Success metrics: data/knowledge asset reuse across practices data quality incidents affecting AI systems (target zero) time from raw data to AI-ready asset

Responsibilities

• Build AI-ready data pipelines and data products that other practices can consume directly
• Design and build knowledge graphs and semantic layers that structure enterprise knowledge for AI consumption
• Own vector and retrieval infrastructure (embeddings, indexes, hybrid search) shared across GenAI and ML practices
• Run data quality assessment and remediation specifically for AI/ML consumption, not just BI
• Own knowledge engineering - taxonomy, ontology, and ingestion pipelines for enterprise knowledge sources
• Partner with GenAI Engineers, Data Scientists, and AI Architects to expose curated data/knowledge as reusable assets
• Explain the difference between BI-grade and AI-grade data quality to non-technical stakeholders
• Act as a shared upstream dependency for multiple practices - manage competing requests
• Document data/knowledge assets clearly enough for self-service reuse

Skills

Data engineering

Knowledge graphs

Semantic modeling

Vector retrieval

Data pipelines

Data governance

Stakeholder communication

Collaboration

Documentation

Tools

Spark

dbt

Airflow

Neo4j

RDF/SPARQL

Job description

Systems Ltd is looking for a Forward Deployed Engineer - Data Management to Make enterprise data and knowledge usable by AI - build AI-ready data products, knowledge graphs, and retrieval infrastructure that every other practice depends on.

KEY RESPONSIBILITIES

• Build AI-ready data pipelines and data products that other practices can consume directly
• Design and build knowledge graphs and semantic layers that structure enterprise knowledge for AI consumption
• Own vector and retrieval infrastructure (embeddings, indexes, hybrid search) shared across GenAI and ML practices
• Run data quality assessment and remediation specifically for AI/ML consumption, not just BI
• Own knowledge engineering - taxonomy, ontology, and ingestion pipelines for enterprise knowledge sources
• Partner with GenAI Engineers, Data Scientists, and AI Architects to expose curated data/knowledge as reusable assets
• Explain the difference between BI-grade and AI-grade data quality to non-technical stakeholders
• Act as a shared upstream dependency for multiple practices - manage competing requests
• Document data/knowledge assets clearly enough for self-service reuse

REQUIREMENTS & SKILLS

• 5-10+ yrs data engineering, with 2+ yrs building AI-ready data products specifically
• Strong in knowledge graph technologies (Neo4j, RDF/SPARQL, or similar) and semantic/ontology modeling
• Experience with vector/retrieval infrastructure (embeddings, ANN indexes, hybrid search)
• Solid data pipeline engineering (Spark, dbt, Airflow, or similar) and data quality frameworks
• Familiarity with enterprise data governance and lineage tooling
• Can explain the difference between BI-grade and AI-grade data quality to a non-technical stakeholder
• Collaborates closely with GenAI Engineers, Data Scientists, and AI Architects as a shared upstream dependency
• Prioritizes competing requests from multiple practices fairly and transparently
• Documents clearly enough that other teams can self-serve without hand-holding
• Success metrics: data/knowledge asset reuse across practices data quality incidents affecting AI systems (target zero) time from raw data to AI-ready asset