Semantic Data and AI Engineer

Job Overview

Join Enterprise Knowledge's Knowledge and Data Services Sector to design and deploy cutting-edge data discovery, integration, and governance solutions. This role involves developing orchestrated data solutions to integrate, enrich, and transform structured and unstructured knowledge assets for Artificial Intelligence (AI) applications. The ideal candidate possesses a passion for diverse data types, a strategic mindset for advising clients on enterprise AI transformations, and the ability to apply new technologies to business challenges.

Key Responsibilities

• Collaborate with data subject matter experts and business users to model domain knowledge.
• Apply NLP techniques (entity extraction, classification, document processing) to transform raw data into AI-ready assets.
• Design, implement, test, and operate end-to-end Retrieval-Augmented Generation (RAG) workflows, including retrieval pipeline architecture and embedding strategies.
• Contribute to Agentic AI solution design, including orchestration patterns, tool use, and memory integration.
• Support business intelligence projects using AI-based solutions.
• Analyze complex datasets and communicate insights to technical and non-technical stakeholders.
• Design and implement data pipelines for ingesting, processing, and enriching content using SQL, Python, R, or equivalent.
• Contribute to semantic layer and knowledge graph implementations within larger AI architectures.
• Contextualize data engineering work for internal and external teams.

Required Qualifications

• Bachelor's degree in math, statistics, economics, data science, computer science, or related field.
• 5+ years of experience in data analysis projects, including report design and production data analysis visualization.
• Proven experience working directly with clients, providing briefings, and presenting work products.
• Experience applying machine learning methods and statistical analysis to business use cases.
• Proficiency in programming languages such as Python, R, or similar for data analysis and modeling.
• Production experience with NLP methods (entity extraction, text classification, document processing).
• Hands-on experience building and optimizing RAG pipelines (embedding strategies, reranking, cross-encoder models).
• Familiarity with retrieval quality metrics (precision, recall, MRR) and evaluation approaches.
• Experience implementing monitoring and observability for RAG and AI components (latency, success rate, data drift).
• Familiarity with data modeling, database architecture, and integration across heterogeneous sources.

Preferred Skills

• Experience with relational and graph databases (including SPARQL).
• Knowledge of AI governance practices (model monitoring, evaluation frameworks).
• Comfortable working in containerized environments (Docker, Kubernetes).
• Experience with cloud platforms (AWS, Azure, or GCP).
• Exposure to ontology or taxonomy design and management tools (e.g., Semantic Sensor Network, PoolParty).
• Experience designing data science projects to meet business requirements.
• Experience implementing agentic AI workflows using frameworks like LangChain, LlamaIndex, LangGraph, or BAML.

Job Details

Location: Arlington, VA, Remote (US)
Job Type: remote
Posted: May 20, 2026