AI Full Stack Engineer

AI Full Stack Engineer 

(AI‑Enabled & Agent‑Driven Development) 

About the role 

We are looking for a AI Full Stack Engineer who builds modern applications using AI as a first‑class capability—both as part of the development workflow and as part of the systems we ship. 

This role goes beyond simply using AI coding assistants. You will have strong understanding of Prompt Engineering, Vibe Coding, Rework Rate Reduction and leverage Custom Agents, and integrations built around the Model Context Protocol (MCP). You will apply strong software engineering fundamentals to assemble, integrate, and operationalize AI capabilities into real production systems. 

The goal of this role is to significantly shorten development and feedback cycles—by automating routine engineering work, augmenting human decision-making, and embedding intelligence directly into our development workflows and the applications we deliver 

What you’ll do 

Core Full Stack Engineering: 

• Contribute to platform enablement, architecture decisions, code reviews, and shared engineering standards 

• Design, build, test, deploy, and operate full stack software solutions 

• Own features end‑to‑end: system design, platform implementation, testing, observability, and lifecycle support 

• Collaborate with business teams to translate requirements into scalable solutions 

AI‑centric full stack development 

• Design and implement Custom AI Agents to support workflows such as:  

• Task automation 

• Decision support and summarization 

• Multi‑step reasoning and tool‑driven execution 

• Fluent in prompt engineering and Vibe Coding Prototypes 

• Build and integrate systems using the 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 

• Integrate applications with external and internal AI services (LLMs, embeddings, search, tools) via well‑designed APIs 

• Harden AI components for 24x7 mission critical physical manufacturing systems with attention to:  

• Reliability and resiliency  

• Latency and performance 

• Security, permissions, and data boundaries 

• Observability and maintainability  

• Utilize retrieval‑augmented generation (RAG) systems  

AI‑enabled development practices 

• Use AI‑assisted tools to accelerate development (code generation, refactoring, test creation, documentation) 

• Critically evaluate and refine AI‑generated output rather than accepting it blindly 

• Apply human judgment to architecture, correctness, and long‑term maintainability 

What we’re looking for 

Core full stack engineering skills 

• Strong foundation in full stack software engineering fundamentals, including object-oriented design, clean code practices, testing strategies, and long-term maintainability. 

• Proven experience building and operating production-grade backend or full-stack systems, including API design, service integration, and data persistence. 

• Ability to design and evolve scalable system architectures, making thoughtful tradeoffs across performance, reliability, security, and developer productivity. 

• Experience working in cloud-native environments, including CI/CD pipelines, containerized deployments, and modern operational practices. 

• Solid understanding of data modeling and storage technologies, spanning relational and non-relational databases, and selecting the right tool for the problem. 

• Track record of owning software end-to-end—from design and implementation through testing, deployment, observability, and lifecycle support. 

• Comfortable collaborating across engineering and business teams to translate requirements into robust, scalable solutions. 

• Technologies you may encounter include: modern backend frameworks (for example C#, ASP.NET), frontend frameworks (for example Angular), RESTful APIs, SQL and NoSQL databases, and Kubernetes-based container platforms. 

AI‑centric skills 

• Experience designing and implementing Custom AI agents beyond simple chat interfaces. This should include experience developing skills for custom agents.  

• AI native skills like Prompt Engineering, Vibe Coding, Context Window management & Chain of thoughts Design 

• Experience integrating systems using Model Context Protocol (MCP) or equivalent structured agent/tool interfaces 

• Strong understanding of prompt design and tool invocation 

• Ability to reason about hallucination risk, failure modes, permissions, and guardrails in AI‑enabled systems 

• Hands‑on experience integrating RAG‑based systems