Full Stack AI Engineer

Responsibilities

• check_circle Design, develop, test, deploy, and maintain scalable full stack applications and platform services. Own software solutions end-to-end, including system design, architecture, implementation, testing, observability, deployment, and lifecycle support.
• check_circle Develop and implement custom AI agents to support workflow automation, decision support, summarization, and multi-step reasoning capabilities.
• check_circle Integrate AI services, large language models (LLMs), embeddings, search technologies, and external tools through secure and scalable APIs.
• check_circle Build and maintain MCP-based integrations to enable structured communication between AI models, tools, and business systems.
• check_circle Collaborate with engineering, product, and business stakeholders to translate requirements into reliable, maintainable, and scalable solutions.
• check_circle Apply AI-assisted development practices while ensuring code quality, architectural integrity, security, reliability, and long-term maintainability.
• check_circle Harden AI components for 24x7 critical physical manufacturing systems with attention to reliability and resiliency, latency and performance, security, permissions, and data boundaries, and observability and maintainability

Basic qualifications

• Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field.
• 5+ years of experience developing production grade full stack or backend software applications, including API design, service integration, and data persistence.
• Hands on experience with cloud-native technologies, CI/CD pipelines, containerization, and Kubernetes based deployments.
• Experience working with both relational and NoSQL databases, including data modeling and storage architecture design.
• Proven experience designing, developing, and deploying AI-enabled applications, including custom AI agents and RAG-based systems.
• Demonstrated experience with prompt engineering and Vibe Coding Prototypes, AI tool integration, and evaluating AI system reliability, security, and failure modes.

Preferred qualifications

• 7+ years of experience building enterprise-scale distributed applications in cloud environments.
• Experience building and integrating Model Context Protocol (MCP) to enable: Structured context sharing between models, tools, and agents; Tool and capability exposure to LLM‑based agents; Interoperability across AI components without hard‑coded integrations
• Proficiency with modern technology stacks such as C#, ASP.NET, Angular, SQL, NoSQL, and Kubernetes.
• Experience developing AI agent skills, tool invocation frameworks, and multi-agent workflow solutions.
• Strong understanding of prompt engineering, context window management, chain-of-thought design, and AI workflow optimization.
• Experience architecting highly available, critical systems with focus on performance, resiliency, observability, and security.
• Track record of leading architecture discussions, conducting code reviews, and establishing engineering standards and guidelines.

About the company