Standard Template (New Job)

Nüvitek•Arlington, VA

4h•$115,000 - $125,000•Remote

About The Position

At Nüvitek, customer success is our Ethos; together, we drive transformational outcomes. We only succeed when our customers succeed. We partner with our customers to achieve business objectives by using our proven customer-centric, value-driven business practices and service delivery methodologies. Nüvitek is seeking a highly skilled Data Engineer to support the design, development, and optimization of advanced AI and data processing solutions. This role will focus on building scalable data pipelines that power large language model (LLM) applications, including retrieval systems, document ingestion workflows, and intelligent search capabilities. The ideal candidate has hands-on experience with retrieval-augmented generation (RAG), contextual augmentation generation (CAG), OCR processing, vector databases, and modern AI data architectures. This role requires strong technical expertise, problem-solving skills, and the ability to work collaboratively within agile pod-based teams. In assuming this position, you will be a critical contributor to meeting Nuvitek's mission: To deliver innovative, cost-effective solutions and services that enable our customers to rapidly adapt to dynamic environments.

Requirements

4+ years of experience in data engineering, data platform development, or AI/ML infrastructure
Strong experience building RAG and/or CAG pipelines
Hands-on experience with vector databases and semantic retrieval systems
Experience developing document ingestion and OCR processing workflows
Strong understanding of LLM integrations and AI data pipeline architectures
Experience working with structured, semi-structured, and unstructured datasets
Proficiency with Python and modern data engineering frameworks
Familiarity with APIs, ETL/ELT pipelines, and distributed processing systems
Experience building and operating data pipelines in secure federal cloud environments, including FedRAMP Moderate and Zero Trust architectures, with appropriate handling of sensitive data and Controlled Unclassified Information (CUI)
Ability to obtain and maintain a federal Public Trust (or higher) clearance
Strong analytical, troubleshooting, and performance optimization skills
Ability to work effectively in agile or pod-based delivery environments
Excellent communication and collaboration skills

Nice To Haves

Experience working with historical archives or large-scale document digitization efforts
Familiarity with cloud-native data platforms and AI infrastructure
Experience with search relevance tuning and ranking optimization
Knowledge of embedding models, chunking strategies, and retrieval optimization techniques
Experience with containerization and orchestration technologies such as Docker and Kubernetes
Familiarity with accessibility, governance, and secure data handling practices
Passion for building scalable AI-driven solutions that improve user experiences and operational efficiency

Responsibilities

Design, develop, and maintain scalable RAG/CAG pipelines for AI-powered applications
Build and optimize document ingestion workflows for structured and unstructured data sources
Manage and maintain vector stores to support semantic search and retrieval capabilities
Develop OCR processing pipelines for historical and modern document collections spanning 1781–2025
Optimize retrieval performance, relevance tuning, and ranking strategies for LLM-based systems
Build reliable data pipelines that support integrations with large language models and AI services
Collaborate with engineers, UX teams, product owners, and stakeholders to deliver scalable AI solutions
Ensure data quality, integrity, security, and performance across ingestion and retrieval systems
Implement monitoring, logging, and troubleshooting for AI and data processing workflows
Contribute to architecture decisions, technical documentation, and engineering best practices
Participate in agile pod-based development teams and continuous improvement initiatives