Full Stack Engineer

*No C2C*

Title:

Full Stack Engineer II

Pay:

$75-$80 per hour

Type:

Hybrid, Plano Texas

Duration:

12-month contract

Position Overview:

We are seeking a hands-on Senior Full Stack Engineer with strong experience in backend development, modern frontend technologies, cloud infrastructure, and AI-powered automation. This role will design, build, and deploy scalable applications and intelligent agentic AI solutions that automate workflows, integrate with enterprise systems, and improve operational efficiency.

The ideal candidate has experience developing production-grade applications using Python, Go, JavaScript, and React; deploying cloud-native services on AWS; and leveraging Model Context Protocol (MCP), agentic AI architectures, and AI-assisted coding tools to accelerate software development and automation.

Key Responsibilities:

• Full Stack Development:

Design, develop, test, and maintain scalable applications using Python, Go, JavaScript, and React across backend services, APIs, and frontend interfaces.

• Agentic AI Engineering:

Design and implement agentic AI solutions, autonomous workflows, tool integrations, and intelligent automation using LLMs and MCP-based architectures.

• MCP Integration:

Build and integrate MCP servers, clients, tools, and context-aware workflows that enable AI agents to interact securely with APIs, databases, and external systems.

• Cloud Infrastructure:

Develop, deploy, and operate cloud-native applications on AWS, with hands-on experience using Amazon ECS and Amazon RDS.

• Containerization & Deployment:

Package and deploy applications using Docker, implement CI/CD pipelines, and improve build, testing, release, and deployment automation.

• API Development & Integration:

Design, build, and maintain secure, reliable RESTful APIs and service integrations that connect frontend applications, backend services, databases, and AI agents.

• AI-Assisted Software Development:

Use AI coding tools such as Claude Code and OpenAI Codex to accelerate development, generate and refactor code, troubleshoot issues, automate repetitive engineering tasks, and improve developer productivity.

• Monitoring & Observability:

Implement application monitoring, logging, alerting, and performance analysis using Amazon CloudWatch and New Relic to maintain reliability and troubleshoot production issues.

• Reliability & Security:

Apply engineering best practices for authentication, authorization, error handling, testing, scalability, and secure handling of application data and AI tool access.

• Cross-Functional Collaboration:

Partner with engineering, product, and business stakeholders to translate requirements into practical software solutions and deliver measurable automation outcomes.

Qualifications:

• Strong professional software engineering experience building and supporting production applications.
• Proficiency in Python and experience with Go and JavaScript; hands-on React development experience.
• Experience designing and integrating APIs, backend services, and distributed application components.
• Hands-on experience deploying and operating applications on AWS, particularly ECS and RDS.
• Experience with Docker, containerized application development, and CI/CD pipelines.
• Practical experience designing or implementing agentic AI solutions, LLM-powered workflows, or AI-driven automation.
• Understanding of MCP and how AI agents can securely access tools, APIs, and contextual data.
• Experience using AI-assisted coding tools such as Claude Code, Codex, or comparable development agents.
• Familiarity with production observability and troubleshooting using CloudWatch, New Relic, or similar monitoring platforms.
• Strong debugging, problem-solving, and communication skills, with the ability to work independently in a fast-paced engineering environment.

Preferred Qualifications:

• Experience deploying MCP-based applications and integrating AI agents with enterprise APIs, databases, and internal tools.
• Familiarity with LLM orchestration, tool calling, structured outputs, agent workflows, and human-in-the-loop controls.
• Experience with automated testing, infrastructure as code, and secure software delivery practices.
• Understanding of AI application security, including least-privilege access, secrets management, input validation, and prompt-injection risks.
• Experience optimizing application performance, cloud resource utilization, and engineering workflows through automation.