Semantic Data and AI Engineer

Enterprise Knowledge•Arlington, VA

2d•$150,000 - $210,000•Hybrid

About The Position

Enterprise Knowledge (EK) is hiring for a full-time Semantic Data and AI Engineer to join our growing Knowledge and Data Services Sector. In this role, you will be responsible for designing and deploying cutting-edge data discovery, integration, and governance solutions for a wide range of organizations around the world. The Semantic Data and AI Engineer will be part of a team working on innovative projects and developing orchestrated data solutions to integrate, enrich, and transform a range of knowledge assets (structured and unstructured) for Artificial Intelligence (AI) solutions. This individual will be able to quickly learn new technologies and apply them to business challenges at large corporations, organizations, and federal agencies. The right candidate will have a passion for working with diverse data types and applying new methods and approaches to data challenges with a strategic mindset to advise clients on enterprise AI transformations. As an EK Semantic Data and AI Engineer, you will join a fast-growing company that is committed to equity and inclusion, have the opportunity to work in a collaborative workplace, take advantage of our unique benefits, and help build our innovative culture. To read more about the impactful work we are doing and to see the latest thought leadership from EK, follow us on LinkedIn.

Requirements

Bachelor’s degree in math, statistics, economics, data science, computer science, or a related field
5+ years experience working on data analysis project(s) designing reports and designing data analysis approaches and visualizations in a production setting
Proven experience working directly with clients, providing briefings, facilitating meetings, 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
Experience with NLP methods: entity extraction, text classification, document processing, or similar techniques applied to unstructured data in a production setting
Hands-on experience building and optimizing RAG pipelines, including embedding strategies, reranking, and cross-encoder models
Familiarity with retrieval quality and evaluation metrics (precision, recall, MRR, and user-centric evaluation approaches)
Experience implementing monitoring and observability for RAG and AI components, including latency, success rate, cache hit rate, retrieval quality, and data drift
Familiarity with data modeling, database architecture, and data integration, aggregation and normalization across heterogeneous sources
Interest in developing as a consultant and taking on additional responsibilities for delivering and growing work

Nice To Haves

Experience designing and working with relational databases
Experience designing and working with graph databases and SPARQL
Experience with AI governance practices covering model monitoring, evaluation frameworks, and access entitlements
Comfortable working with containerized environments; Docker proficiency expected, Kubernetes familiarity a plus
Experience with cloud platforms (AWS, Azure, or GCP) for deploying and operating AI and data solutions
Exposure to ontology or taxonomy design, and familiarity with taxonomy/ontology management tools (Progress Semaphore, PoolParty, Synaptica, Mondeca, etc.)
Experience designing and planning data science projects to meet business requirements
Experience implementing agentic AI workflows using frameworks such as LangChain, LlamaIndex, LangGraph, BAML, or equivalent

Responsibilities

Work with data subject matter experts and business users to effectively understand and model their domain of knowledge
Apply NLP techniques (entity extraction, classification, and document processing) to transform raw data and content into AI-ready assets
Design, implement, test, and operate end-to-end RAG workflows for client engagements, including retrieval pipeline architecture, embedding strategies, and response evaluation
Contribute to agentic AI solution design and implementation, including orchestration patterns, tool use, and memory and retrieval integration
Support a variety of business intelligence projects using AI-based solutions
Analyze complex datasets and communicate insights to both technical and non-technical stakeholders
Design and implement data pipelines for ingesting, processing, and enriching structured and unstructured content using SQL, Python, R, or equivalent languages
Contribute to semantic layer and knowledge graph implementations as part of larger AI solution architectures
Work with internal and external teams to contextualize data engineering work into larger project context