Forward Deployed Engineer - Data Management

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

Job summary

Systems Ltd is seeking a Forward Deployed Engineer – Data Management to enable AI-ready data production. You will build data products, design knowledge graphs, and manage retrieval infrastructure to serve multiple AI/ML practices.

You will collaborate with GenAI Engineers and Data Scientists to expose curated data as reusable assets and ensure enterprise data quality for AI consumption.

Qualifications

• 5–10+ yrs data engineering experience with 2+ yrs building AI-ready data products.
• Experience building AI-ready data products for AI/ML.
• Strong knowledge of knowledge graphs and semantic modeling.
• Experience with vector and retrieval infrastructure.

Responsibilities

• Build AI-ready data pipelines and data products used by other practices.
• Design knowledge graphs and semantic layers to structure enterprise knowledge for AI consumption.
• Own vector and retrieval infrastructure including embeddings, indexes, and hybrid search.
• Conduct data quality assessment and remediation for AI/ML consumption.
• Own knowledge engineering including taxonomy, ontology, and ingestion pipelines.
• Collaborate with GenAI Engineers, Data Scientists, and AI Architects to expose data as reusable assets.
• Explain the difference between BI-grade and AI-grade data quality to non-technical stakeholders.
• Serve as a shared upstream dependency and manage multiple competing requests.
• Document data/knowledge assets for self-service reuse.

Skills

Data engineering

Knowledge graphs

Semantic modeling

Vector retrieval

Spark

dbt

Airflow

Data governance

Stakeholder communication

Tools

Neo4j

RDF/SPARQL

Embeddings

ANN indexes

Hybrid search

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