Forward Deployed Engineer - Data Management

Role Overview

At Systems Ltd, we are seeking a Forward Deployed Engineer specializing in Data Management to transform enterprise data into AI-ready assets. This role focuses on building robust AI-driven data products, knowledge graphs, and retrieval systems that serve various AI and machine learning teams across the organization.

Key Responsibilities

• Develop AI-centric data pipelines and products that are consumable by other teams and practices.
• Design and implement knowledge graphs and semantic layers to organize enterprise knowledge for AI utilization.
• Manage vector-based retrieval systems including embeddings, indexes, and hybrid search infrastructures supporting Generative AI and ML practices.
• Conduct data quality evaluations tailored specifically for AI/ML applications, beyond traditional Business Intelligence requirements, and remediate issues as needed.
• Lead knowledge engineering efforts involving taxonomy creation, ontology development, and data ingestion workflows targeting enterprise knowledge sources.
• Collaborate effectively with GenAI Engineers, Data Scientists, and AI Architects to make curated data and knowledge assets readily reusable.
• Communicate distinctions between BI-standard and AI-standard data quality clearly to non-technical stakeholders.
• Serve as a primary shared upstream resource coordinating and managing multiple requests from various practices in a fair and transparent manner.
• Document all data and knowledge assets comprehensively to facilitate self-service usage by other teams without requiring direct support.

Required Qualifications and Skills

• 5 to 10+ years of experience in data engineering, with a minimum of 2 years specifically focused on creating AI-ready data products.
• Expertise in knowledge graph technologies such as Neo4j, RDF/SPARQL, or related semantic and ontology modeling methods.
• Proficient in vector retrieval infrastructure technologies, including embeddings, Approximate Nearest Neighbor (ANN) indexes, and hybrid search systems.
• Strong background in data pipeline engineering using tools like Spark, dbt, Airflow, or comparable frameworks, along with solid experience in data quality management.
• Knowledge of enterprise data governance policies and lineage tracking tools.
• Ability to clearly differentiate and explain BI-grade data quality versus AI-grade data quality to stakeholders without technical backgrounds.
• Collaborative work style partnering closely with AI teams as a critical upstream dependency.
• Capability to prioritize and balance competing demands from multiple business practices transparently.
• Strong documentation skills to enable autonomous data asset reuse by other teams.

Success Indicators

• Extensive reuse of data and knowledge assets across multiple practices.
• Zero tolerance for data quality incidents impacting AI systems.
• Efficient turnaround time from raw data acquisition to AI-ready asset creation.