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Model Context Protocol Jobs in Virginia (NOW HIRING)

Integral

Agentic Engineer

Tysons Corner, VA · On-site

$90K - $175K/yr

Implement and leverage the Model Context Protocol (MCP) to seamlessly connect our AI models with external tools, APIs, and data sources. * Move beyond prototypes by building rigorous evaluation ...

Integral

Agentic Engineer

Tysons, VA · On-site

$90K - $175K/yr

Implement and leverage the Model Context Protocol (MCP) to seamlessly connect our AI models with external tools, APIs, and data sources. * Move beyond prototypes by building rigorous evaluation ...

Halvik

Sr. Security Engineer

Alexandria, VA · On-site

$155K - $165K/yr

... and Model Context Protocol configuration files). The Senior Security Engineer will serve as a technical lead on the program, working closely with customer stakeholders, the COR/CO, and Halvik ...

EchoStar

Senior AI Engineer

Herndon, VA · On-site

$110K - $137K/yr

Build and deploy autonomous AI agents and securely grounded RAG pipelines using Amazon Bedrock, OpenSearch, and Model Context Protocol (MCP) to execute complex business tasks * Implement Guardrails ...

New

Tetra Tech

Sr. Security Engineer

Alexandria, VA · On-site

$140 - $190/hr

... and Model Context Protocol configuration files). The Senior Security Engineer will serve as a technical lead on the program, working closely with customer stakeholders, the COR/CO, and Halvik ...

EchoStar

Senior AI Engineer

Herndon, VA · On-site

$110K - $137K/yr

Build and deploy autonomous AI agents and securely grounded RAG pipelines using Amazon Bedrock, OpenSearch, and Model Context Protocol (MCP) to execute complex business tasks * Implement Guardrails ...

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All JobsModel Context Protocol JobsModel Context Protocol Jobs in Virginia

Model Context Protocol information

What is the difference between Model Context Protocol vs Data Analyst?

AspectModel Context ProtocolData Analyst
Required CredentialsKnowledge of data modeling, API protocols, and software developmentBachelor's degree in statistics, mathematics, or related field
Work EnvironmentTechnical teams, software development, AI/ML projectsBusiness intelligence, reporting, data visualization
Industry UsageTech, AI, software developmentFinance, marketing, healthcare, business
Search & Comparison IntentUnderstanding technical protocols for AI modelsAnalyzing data for insights and decision-making

The Model Context Protocol focuses on technical data exchange and AI model integration, requiring programming and API knowledge. In contrast, Data Analysts interpret data to generate reports and insights, often working with business tools. Both roles involve data but serve different functions within organizations.

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Model Context Protocol jobs near you
Infographic showing various Model Context Protocol job openings in Virginia as of August 2026, with employment types broken down into 2% As Needed, 84% Full Time, 12% Part Time, and 2% Contract. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution.

Python/AI Full Stack Developer

Elite IT Solutions inc

Ashburn, VA • On-site

Other

Posted 4 days ago

Job description

Hi,
Please find the job description below
Role: Python/AI Full Stack Developer
Location: Remote
Job Description
•    The Senior Full-Stack Engineer is a hands-on, deeply technical position responsible for designing, building, and deploying cutting-edge Generative AI software and multi-agent systems.
•    This role owns the end-to-end implementation of the foundation's intelligent applications, from engineering advanced backend services on Google Cloud Vertex AI utilizing Gemini Enterprise models, to orchestrating complex workflows with Google’s Agent Development Kit (ADK) and connecting enterprise content and data through the Model Context Protocol (MCP).
•    The engineer will also build interactive web user experiences in React and manage structural and semantic vector data in PostgreSQL.
Qualifications
•    Design, develop, and deploy enterprise-scale multi-agent systems using Google’s Agent Development Kit (ADK), including multi-step autonomous workflows, API and tool calling, stateful conversation management, and human-in-the-loop patterns.
•    Build and maintain Model Context Protocol (MCP) clients and servers to provide standardized connectivity between LLM applications, enterprise content repositories, data platforms, APIs, and web services.
•    Design scalable full-stack architectures connecting AI services, application logic, relational and vector data stores, and interactive web applications.
•    Integrate Gemini models through Google Cloud Vertex AI, configuring context management, prompt templates, structured outputs, model behavior, and appropriate safety controls.
•    Develop clean, maintainable, production-grade Python services supporting application logic, AI orchestration, LangChain workflows, and data integration pipelines.
•    Build modern, responsive web applications using React and JavaScript/TypeScript, including interfaces capable of streaming and displaying agent states and model responses.
•    Design and optimize data solutions using PostgreSQL, pgvector, and BigQuery, supporting relational, keyword, semantic vector, and hybrid search use cases.
•    Develop and optimize Retrieval-Augmented Generation (RAG) pipelines that provide LLMs and agents with accurate, relevant enterprise context.
•    Containerize and deploy full-stack AI applications within Google Cloud Platform, incorporating automated testing and modern CI/CD practices.
•    Monitor and optimize token consumption, model selection and routing, semantic caching, application latency, model accuracy, and cloud costs.
•    Implement evaluation and observability capabilities for AI applications, including agent tracing, response quality monitoring, hallucination detection, model drift identification, and dynamic routing evaluation.
•    Implement AI security controls to mitigate risks including prompt injection, sensitive-data leakage, and unauthorized PII exposure.
•    Apply responsible AI engineering practices, including automated validation and evaluation of prompts and model responses for quality, transparency, fairness, and reliability.
•    Work within an Agile delivery environment and independently take solutions from initial design through development, testing, deployment, and production support.
•    Collaborate with architects, engineers, product teams, and other stakeholders to translate business requirements into scalable technical solutions.
Qualifications and Skills
•    6+ years of professional software engineering experience, with a background in full-stack development, software architecture, machine learning engineering, or a related discipline.
•    2+ years of hands-on Generative AI experience, building and deploying enterprise LLM applications, RAG solutions, or agentic/multi-agent systems into production environments.
•    Advanced programming skills in Python, including experience developing production-grade backend applications and services.
•    Strong hands-on experience with Google Cloud Platform (Google Cloud Platform) and Vertex AI.
•    Production experience working with Gemini or comparable enterprise LLM platforms.
•    Practical experience developing agentic AI solutions using Google Agent Development Kit (ADK) or comparable agent orchestration frameworks.
•    Experience implementing or working with Model Context Protocol (MCP) clients and/or servers.
•    Strong understanding of RAG architectures, embeddings, vector search, context management, prompt engineering, and LLM orchestration.
•    Experience with LangChain or similar AI application frameworks.
•    Proficiency with React and modern JavaScript/TypeScript for developing single-page web applications and integrating streaming APIs.
•    Strong knowledge of PostgreSQL, including relational data modeling, advanced querying, and vector indexing/search using pgvector or similar technologies.
•    Experience with BigQuery or comparable cloud data platforms.
•    Working knowledge of Docker, automated testing, CI/CD pipelines, and cloud-native application deployment.
•    Understanding of Generative AI observability, evaluation, security, model performance, token optimization, and cost management.
•    Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Software Engineering, or a related discipline, or equivalent practical experience.
Preferred Skills
•    Experience designing enterprise-scale autonomous or multi-agent AI systems.
•    Strong understanding of agent memory, tool calling, workflow orchestration, and human-in-the-loop architectures.
•    Experience implementing hybrid search combining traditional keyword retrieval and semantic vector search.
•    Familiarity with LLM evaluation frameworks, tracing, hallucination detection, and model quality monitoring.
•    Knowledge of responsible AI practices and security considerations specific to enterprise Generative AI applications.
•    Experience taking AI applications from proof of concept through scalable production deployment.
 

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