AI Full-Stack Engineer

Job Description

Position Summary

The AI Full-Stack Engineer is responsible for designing, developing, implementing, and supporting enterprise AI solutions and modern business applications. This role combines full-stack software engineering expertise with artificial intelligence technologies to build secure, scalable, and reliable solutions that improve business operations, automate workflows, and enhance employee productivity.

Working closely with business stakeholders, product managers, architects, and IT teams, the AI Full-Stack Engineer translates business requirements into innovative AI-powered applications, intelligent agents, APIs, and integrations while ensuring solutions align with enterprise architecture, security standards, and AI governance policies.

Essential Responsibilities

AI Solution Development

• Design, develop, deploy, and maintain AI-powered applications, intelligent agents, and automation solutions.
• Build AI workflows utilizing Large Language Models (LLMs), Retrieval Augmented Generation (RAG), prompt engineering, semantic search, and vector databases.
• Develop reusable AI components, prompt libraries, templates, and services to accelerate future development.
• Integrate AI capabilities into existing business applications and enterprise platforms.
• Evaluate emerging AI technologies and recommend practical solutions that deliver measurable business value.
• Monitor AI solution performance and continuously improve accuracy, reliability, cost efficiency, and user experience.

Full-Stack Application Development

• Design, develop, test, and maintain modern web applications using front-end and back-end technologies.
• Build secure RESTful APIs, microservices, and system integrations.
• Develop responsive user interfaces that provide intuitive access to AI-powered capabilities.
• Create scalable application architectures that support enterprise growth and future enhancements.
• Troubleshoot and resolve application issues while ensuring high availability and performance.

Data Integration & Engineering

• Develop integrations with enterprise applications, databases, document repositories, and cloud services.
• Build data pipelines supporting AI applications and intelligent automation.
• Design and maintain vector indexes and semantic search capabilities.
• Ensure data quality, integrity, and security across all integrations.

Cloud & DevOps

• Deploy and support applications across cloud and hybrid environments.
• Develop and maintain CI/CD pipelines to automate application deployments.
• Utilize containerization and orchestration technologies to improve scalability and reliability.
• Monitor application health, logging, and operational performance.

Security, Governance & Compliance

• Develop solutions that comply with enterprise security, privacy, and regulatory requirements.
• Implement authentication, authorization, encryption, and secure coding practices.
• Follow established AI governance standards, responsible AI principles, and data management policies.
• Participate in architecture, security, and code reviews.

Collaboration

• Partner with business leaders and subject matter experts to identify opportunities for AI-enabled process improvements.
• Work closely with COO, Product Manager, Director of IT, and Software Developers throughout the solution lifecycle.
• Prepare technical documentation, solution designs, and operational procedures.
• Share knowledge and contribute to development standards and best practices.

Required Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related discipline, or equivalent professional experience.
• Minimum of five years of experience developing enterprise software applications.
• Minimum of two years of experience building AI-enabled applications or implementing generative AI solutions.
• Experience delivering production-ready applications using modern software development practices.
• Strong analytical, problem-solving, organizational, and communication skills.
• Ability to manage multiple priorities in a fast-paced environment.

Technical Qualifications

AI Engineering

• Experience developing AI-powered applications and intelligent agents.
• Large Language Models (Anthropic Claude).
• Prompt engineering and prompt optimization.
• Retrieval Augmented Generation (RAG).
• AI orchestration frameworks.
• Model Context Protocol (MCP).
• Function call…