Staff AI Full Stack Engineer

About our Team:
We are seeking an experienced AI Full Stack Engineer to build modern applications where AI is a first-class capability—both in the development workflow and in production systems. This role focuses on designing and integrating AI-powered solutions using custom agents, Model Context Protocol (MCP), and scalable software engineering practices to accelerate development and deliver reliable, production-grade systems. Our team needs additional support to scale with the increased SMAI Data Science footprint and expand software support for Technology Development workflows and AI enablement.

Position Overview:
We are looking for an AI Full Stack Engineer who builds modern applications using AI as a first-class capability—both as part of the development workflow and the systems we ship. This role goes beyond simply using AI coding assistants. It requires strong understanding of advanced timely creation, vibe scripting, Rework Rate Reduction, and applying Custom Agents with integrations built around MCP or equivalent experience. The goal is to significantly shorten development and feedback cycles by automating routine engineering work, augmenting human decision-making, and embedding intelligence into development workflows and delivered applications.

Responsibilities:

• Contribute to platform enablement, architecture decisions, code reviews, and shared engineering standards; design, build, test, deploy, and operate full stack software solutions while owning features end‑to‑end including system design, implementation, testing, observability, and lifecycle support, and collaborating with business teams to translate requirements into scalable solutions

• Build and implement Custom AI Agents to support workflows like task automation, decision support, summarization, multi-step reasoning, and guided execution. Show proficiency in timely 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, and interoperability across AI components without hard‑coded integrations

• Integrate applications with external and internal AI services (LLMs, embeddings, search, tools) via well‑designed APIs, and harden AI components for 24x7 critical physical manufacturing systems with attention to reliability, resiliency, latency, performance, security, permissions, data boundaries, observability, and maintainability while applying retrieval‑augmented generation (RAG) systems

• Use AI‑assisted tools to accelerate development (code generation, refactoring, test creation, documentation), critically evaluate and refine AI‑generated output, and apply human judgment to architecture, correctness, and long‑term maintainability

• Conduct requirements gathering and analysis to understand software domains, interfaces, and characteristics; write code using programming, scripting, and database languages; support testing, deployment, maintenance, and evolution; and follow guidelines including coding standards, code reviews, source control, build processes, testing, and operations.

Minimum Qualifications:

• 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 with 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 (relational and non‑relational) and selecting the right tool for the problem

• Track record of owning software end‑to‑end from design through lifecycle support and collaborating across engineering and business teams

• Experience with technologies such as C#, ASP.NET, Angular, RESTful APIs, SQL and NoSQL databases, and Kubernetes‑based platforms

• Experience designing and implementing Custom AI agents beyond simple chat interfaces, including developing skills for custom agents

• AI‑native skills such as Prompt Engineering, Vibe Coding, context window management, and chain‑of‑thought design

• Experience integrating systems using MCP or equivalent structured agent/tool interfaces and strong understanding of prompt design and tool invocation

• Ability to reason about hallucination risk, failure modes, permissions, and guardrails in AI‑enabled systems, and hands‑on experience integrating RAG‑based systems

Preferred Qualifications:

• Experience applying AI‑enabled development practices to accelerate engineering workflows and improve productivity

• Demonstrated ability to critically evaluate AI‑generated outputs and refine them for production‑grade systems

• Experience building interoperable AI systems without hard‑coded integrations

• Strong understanding of operationalizing AI in critical environments with high reliability and performance requirements