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Ai Agent Engineer Jobs in Reston, VA (NOW HIRING)

Tror AI for everyone

AI Agent Engineer

Mclean, VA · On-site

Job Title: - Senior AI Agent Engineer Location: - McLean, VA - 5 days onsite Employment Type: - Contract Need 10+ years of experience resumes and need locals to VA or nearby location. - We are ...

Klaxontech

Senior AI Agent Engineer - Agentic

Reston, VA · On-site

$108K - $149K/yr

AI Engineer with expertise building agentic software development platforms using AWS, Strands Agents, Bedrock, Neptune, RDS, Step Functions, and Python. Experienced in code understanding ...

Trilagen

Senior Sales Executive - Agentic AI

Reston, VA · On-site

Cross-sell across all three Trilagen practices - AI Agent Engineering, Cloud (AWS & Oracle OCI), and Identity Security (Okta & SailPoint ISC). Every engagement is a door into the other two. Territory ...

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How much do ai agent engineer jobs pay per hour?

As of Aug 11, 2026, the average hourly pay for ai agent engineer in Reston, VA is $55.80, according to ZipRecruiter salary data. Most workers in this role earn between $45.00 and $64.76 per hour, depending on experience, location, and employer.

What is an AI Agent engineer?

An AI Agent Engineer designs, develops, and optimizes autonomous AI agents that can perceive, reason, and act within digital or real-world environments. They work with machine learning, reinforcement learning, and natural language processing to build intelligent systems that interact dynamically with users and other agents. These engineers often integrate AI models with APIs, databases, and software applications to create functional and scalable AI-driven solutions. Their role bridges software engineering and artificial intelligence, ensuring agents operate efficiently and adaptively in various use cases.

What are some typical challenges faced by AI Agent engineers in their day-to-day work?

AI Agent Engineers often encounter challenges such as optimizing agent performance for real-time environments, integrating new algorithms without disrupting existing systems, and ensuring their models can handle diverse and dynamic real-world data. Troubleshooting unexpected behaviors or edge cases and keeping up with rapidly evolving research in artificial intelligence can also be demanding. Working closely with product managers, data scientists, and software developers helps address these challenges, making collaboration and adaptability key components of daily tasks. Overcoming these issues offers valuable learning opportunities and contributes to the growth of robust, innovative AI solutions.

What are the key skills and qualifications needed to thrive as an AI Agent engineer?

To excel as an AI Agent Engineer, you need a robust background in computer science, machine learning, and software engineering, often supported by a degree in a STEM field. Familiarity with programming languages like Python, relevant AI frameworks (such as TensorFlow or PyTorch), and experience with cloud platforms are typically expected, along with certifications in AI or data science being advantageous. Strong problem-solving abilities, effective communication, and teamwork skills are essential soft skills for collaborating on complex AI projects. These competencies ensure you can design, implement, and refine intelligent agents that meet real-world challenges and deliver business value.

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Ai Agent Engineer jobs near you

AI Agent Engineer

Tror AI for everyone

Mclean, VA • On-site

Contractor

Re-posted 22 days ago

Job description

Job Title: - Senior AI Agent Engineer

Location: - McLean, VA - 5 days onsite

Employment Type: - Contract

Need 10+ years of experience resumes and need locals to VA or nearby location.

Job Description: -

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