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Entry Level Ai Agent Developer Jobs in Virginia (NOW HIRING)

Booz Allen Hamilton

AI Engineer

Arlington, VA

$77K - $176K/yr

R0241255 AI Engineer The Opportunity: As a programmer, you know that machine learning is critical ... Experience with AI agent frameworks including LangChain or LlamaIndex * Experience with open-source ...

Booz Allen Hamilton

AI Engineer

Arlington, VA

$77K - $176K/yr

AI Engineer The Opportunity: As a programmer, you know that machine learning is critical to ... Experience with AI agent frameworks including LangChain or LlamaIndex * Experience with open-source ...

Booz Allen Hamilton, Inc.

AI Engineer

Arlington, VA · On-site

$77K - $176K/yr

AI Engineer The Opportunity: As a programmer, you know that machine learning is critical to ... Experience with AI agent frameworks including LangChain or LlamaIndex * Experience with open-source ...

Booz Allen Hamilton, Inc.

AI Engineer

Arlington, VA · On-site

$77K - $176K/yr

Share AI Engineer The Opportunity: As a programmer, you know that machine learning is critical to ... Experience with AI agent frameworks including LangChain or LlamaIndex * Experience with open-source ...

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Entry Level Ai Agent Developer information

What are Entry Level AI Agent Developers?

Entry Level AI Agent Developers are professionals who assist in designing, building, and maintaining artificial intelligence agents, such as chatbots or virtual assistants, often under the supervision of more experienced engineers. They typically work with programming languages like Python and use machine learning frameworks to help create intelligent systems that can interact with users or perform tasks autonomously. These roles are suited for those new to the field and often require a foundational understanding of AI concepts, basic coding skills, and a willingness to learn advanced topics on the job.

What are the key skills and qualifications needed to thrive as an Entry Level AI Agent Developer, and why are they important?

To thrive as an Entry Level AI Agent Developer, you need a solid understanding of programming languages (such as Python), basic machine learning concepts, and a relevant degree in computer science or a related field. Familiarity with AI frameworks (like TensorFlow or PyTorch), API integration, and version control systems (e.g., Git) is typically required. Problem-solving ability, eagerness to learn, and effective teamwork are standout soft skills for this role. These skills and qualities are vital for building, maintaining, and improving AI agents in a collaborative and rapidly evolving tech environment.

What are some typical projects or tasks an Entry Level AI Agent Developer can expect to work on during their first year?

As an Entry Level AI Agent Developer, you can expect to work on tasks such as building and fine-tuning conversational AI agents, assisting with data preprocessing, and implementing basic machine learning models under the guidance of senior engineers. You’ll likely contribute to updating or troubleshooting existing AI agents, performing code reviews, and writing test cases. Collaboration with data scientists, UX designers, and product managers is common, giving you exposure to the full development lifecycle and valuable opportunities to learn from experienced colleagues.

What is the difference between Entry Level Ai Agent Developer vs Data Analyst?

AspectEntry Level Ai Agent DeveloperData Analyst
Required CredentialsBachelor's in CS, AI, or related field; basic programming skillsBachelor's in Statistics, Math, or related field; data analysis skills
Work EnvironmentTech companies, AI startups, R&D labsBusiness, finance, healthcare sectors
Employer & Industry UsageDeveloping AI agents, chatbots, automation toolsInterpreting data, creating reports, supporting decision-making

Entry Level Ai Agent Developers focus on creating and refining AI agents and chatbots, often requiring programming and AI knowledge. Data Analysts interpret data to inform business decisions, typically with statistical skills. While both roles involve data and technology, they serve different functions within organizations. The choice depends on your interest in AI development versus data interpretation.

What are the most commonly searched types of Ai Agent Developer jobs in Virginia? The most popular types of Ai Agent Developer jobs in Virginia are:
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What cities in Virginia are hiring for Entry Level Ai Agent Developer jobs? Cities in Virginia with the most Entry Level Ai Agent Developer job openings:
Entry Level Ai Agent Developer jobs near you
Infographic showing various Entry Level Ai Agent Developer job openings in Virginia as of June 2026, with employment types broken down into 15% Internship, and 85% Full Time. Highlights an 86% In-person, and 14% Remote job distribution.

Senior AI Agent Engineer - SDLC Optimization

Cliff Services Inc

Mclean, VA • On-site

$107K - $147K/yr

Other

Posted 11 days ago

Job description

Job Title: Senior AI Agent Engineer - SDLC Optimization
Job Summary
We are seeking a forward-thinking AI SDLC Engineer to transform our development processes from linear automation to an agentic, multi-agent workflow.
The ideal candidate will design, build, and deploy specialized AI subagents (e.g., using frameworks like LangChain, CrewAI, or Claude Code) that collaborate to create production-grade software.
You will enable AI to handle specialized tasks-architecture planning, frontend/backend development, debugging, and security reviews-while maintaining context isolation.
Responsibilities
Subagent Architecture & Creation: Design and implement task-specific AI subagents (e.g., ""Code Reviewer,"" ""SQL Expert,"" ""Frontend Builder"") with dedicated system prompts, tool access, and context boundaries.
Orchestration & Workflow Automation: Build orchestration logic to manage the lifecycle of subagents (initialization, delegation, execution, and cleanup) using agentic frameworks.
AI-Driven SDLC Integration: Implement agentic workflows across the entire SDLC, from requirements gathering and architecture design to automated testing, code review, and deployment.
Context & Memory Management: Optimize AI performance by isolating noisy, long-running tasks into subagents, ensuring the main agent remains efficient and context-aware.
Tooling & Integration: Integrate AI agents with internal development tools, including Bitbucket, Jira, ServiceNow and CI/CD pipelines.
Performance Evaluation: Monitor, evaluate, and refine agentic workflows to improve code quality, reduce toil, and accelerate release cycles.
Location: McLean, VA (locals preferred)
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