Find Jobs
Find Jobs Near You – Available Work in Your Location
Skip to job details
BT
Bespoke Technologies Inc.
AI Architect
Career Insights for Natural Language Processing Engineer
See where this job fits in the broader career landscape. Knowing your career path helps you see what's possible from here.
Scorecard
Based on Virginia data
Review key factors to help you decide if this role fits your goals. How is this calculated?
What they do
A Natural Language Processing Engineer specializes in developing and implementing algorithms and models tailored for understanding, processing, and generating natural language text. They utilize methodologies such as tokenization, parsing, named entity recognition, part-of-speech tagging, and other NLP techniques to perform tasks including text classification, chatbot development, and other applications where the primary input or output is natural language text.
$120,773 / year median in Virginia
Job Description
Job Requirements Tysons, VA Top Secret/SCI Full Scope Polygraph Mid Level Career (5+ yrs experience) Salary not specified Join Premium to unlock estimated salaries Job Description
BT-429 - AI
Architect Location:
Tysons, VA Bespoke Technologies is seeking an experienced AI Architect to design, architect, prototype, and implement advanced Artificial Intelligence and Generative AI solutions supporting mission-critical data and technology initiatives. The AI Architect will work closely with senior mission stakeholders, data scientists, software engineers, and technical teams to translate complex requirements into scalable AI architectures and solutions. The ideal candidate will possess strong Python development skills, a deep understanding of LLMs and Generative AI architecture, and experience designing AI systems that integrate data, APIs, cloud services, AI agents, and modern GenAI capabilities. The role requires a strong understanding of how AI systems interact with structured and unstructured data, including OCR, knowledge graphs, embeddings, vector-based architectures, and data pipelines. Responsibilities- Design, architect, and prototype AI and Generative AI solutions that address complex mission and customer requirements.
- Develop AI architectures leveraging Large Language Models (LLMs), Generative AI, AI agents, and modern AI services.
- Develop solutions using Python for AI engineering, data processing, integrations, and prototyping.
- Apply prompt engineering techniques to optimize LLM performance and develop effective AI applications.
- Understand LLM context windows, tokenization, embeddings, and embedding models and how they impact AI application design.
- Design and implement AI agents, including agent orchestration, tool calling, and function calling.
- Configure, integrate, and interact with MCP (Model Context Protocol) servers to provide AI systems with access to external tools, services, and data.
- Design and integrate APIs supporting AI applications and interactions between services, models, and data sources.
- Incorporate OCR capabilities into AI architectures for extracting and processing information from documents and other unstructured data.
- Develop and leverage knowledge graphs, including an understanding of entities, relationships, and how they can support AI reasoning and information retrieval.
- Design architectures incorporating embedding models, vector databases, semantic search, and retrieval-based AI approaches.
- Design and integrate AI-powered chatbot and conversational interfaces.
- Develop mechanisms for source traceability, information verification, and reduction of AI hallucinations.
- Evaluate structured and unstructured data and determine the appropriate approach for preparing and making data available to AI systems.
- Design solutions that integrate ETL pipelines, batch processing, data lakes, and streaming data.
- Develop an understanding of data schemas and data models and how they support AI and data processing systems.
- Work with data ingestion and orchestration technologies such as Apache NiFi and Airflow.
- Design and deploy AI solutions using AWS cloud architecture, including services such as S3, EC2, IAM, VPC, Amazon Bedrock, and related AWS services.
- Evaluate existing data holdings, technical environments, and mission use cases to determine appropriate AI architecture and data strategies.
- Collaborate directly with high-level and senior customers to understand mission needs, communicate technical concepts, and recommend appropriate AI solutions.
- Lead technical discussions and communicate complex AI architecture concepts to both technical and non-technical stakeholders.
- Prototype and evaluate emerging AI technologies and determine their applicability to mission requirements.
Here is what you need:
- Strong Python
- Understanding of LLMs / GenAI architecture
- Understanding of OCR
- Understanding of Knowledge graphs and entities/relationships
- APIs
- Prompt Engineering
- Context Windows and Tokenization
- Embedding Models
- Agents and tool calling
- Setting up and hitting MCP Servers
- Function calling
- Good understanding of Cloud Architecture with AWS (S3, EC2, IAM, VPC, Bedrock, etc...)
- Understanding of ETL Pipeline and Batch Processing o Data Lakes o Streaming data o NiFi and Airflow o Understand data schemas / models
- Ability to work with high level/senior customers Bonus if you have:
- Experience developing or deploying GenAI agents in mission-driven environments.
- Experience with vector databases and semantic search.
- Experience with AI model optimization and evaluation.
- Experience designing cloud-native AI platforms and distributed systems.
- Experience implementing source traceability and verification mechanisms for AI-generated content.
- Experience with data cataloging, metadata management, and knowledge management.
- Experience with Java or other modern programming languages.
- AI, cloud, data science, or software engineering certifications.
Education:
- Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related technical field.