Forward Deployed Engineer - Data Management

Systems Limited; Lahore; On-site; PKR 1,800,000 - 3,000,000; Full time

Job summary

Systems Ltd is seeking a Forward Deployed Engineer – Data Management to make enterprise data usable by AI by building AI-ready data products, knowledge graphs, and retrieval infrastructure used across GenAI and ML practices.

The role emphasizes owning data pipelines, semantic modeling, vector retrieval, and data governance. Collaboration with data scientists and AI architects is essential to deliver reusable data assets and ensure AI-grade data quality.

Qualifications

• 5–10+ yrs data engineering with 2+ yrs building AI-ready data products.
• Strong knowledge graph technologies (Neo4j, RDF/SPARQL) and semantic/ontology modeling.
• Experience with vector/retrieval infrastructure (embeddings, ANN indexes, hybrid search).
• Solid data pipeline engineering (Spark, dbt, Airflow) and data quality frameworks.
• Familiarity with enterprise data governance and lineage tooling.
• Ability to explain BI-grade vs AI-grade data quality to non-technical stakeholders.
• Collaborates with GenAI Engineers, Data Scientists, and AI Architects as upstream dependencies.
• Prioritizes competing requests across practices in a fair, transparent way.
• Documents clearly so teams can self-serve without hand-holding.
• Success metrics: asset reuse, data quality incidents, time from raw data to AI-ready asset.

Responsibilities

• Build AI-ready data pipelines and data products for consumption by other practices.
• Design and build knowledge graphs and semantic layers for AI use.
• Own vector and retrieval infrastructure (embeddings, indexes, hybrid search).
• Run data quality assessment and remediation for AI/ML consumption.
• Own knowledge engineering — taxonomy, ontology, and ingestion pipelines.
• 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 upstream dependency for multiple practices and manage competing requests.
• Document data/knowledge assets clearly for self-service reuse.

Skills

Data engineering

Knowledge graphs

Vector retrieval

Data pipelines

Data governance

Communication about data quality

Cross-team collaboration

Prioritization

Documentation

Data/knowledge asset metrics

Tools

Neo4j

RDF/SPARQL

Spark

dbt

Airflow

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