Full Stack AI Engineer
First seen by Alion on Oct 19, 2026.
Responsibilities:
- Build and maintain web applications, APIs, and services using Python.
- Develop and improve AI applications using large language models (LLMs), including Retrieval-Augmented Generation (RAG) solutions.
- Design document-ingestion, chunking, embedding, retrieval, and response-generation workflows.
- Design and develop tools for AI applications, including function-calling workflows and integrations using the Model Context Protocol (MCP).
- Integrate vector databases and search technologies into production systems.
- Develop responsive frontend features and connect them to backend APIs.
- Evaluate AI output quality, retrieval relevance, latency, and cost; improve performance and reliability.
- Implement authentication, authorization, logging, testing, and monitoring.
- Apply software engineering best practices, including SOLID principles and design patterns, to build scalable, maintainable systems.
- Deploy and operate LLMs in on-premises environments using inference servers such as vLLM or SGLang.
- Collaborate with product, design, and engineering teams to turn requirements into production-ready features.
- Document technical decisions and contribute to code reviews and engineering standards.
Required Qualifications:
- Professional experience developing full-stack web applications.
- Strong Python skills and experience with frameworks such as FastAPI, Django, or Flask.
- Experience designing and building REST APIs or similar service interfaces.
- Hands-on experience developing LLM-powered applications and RAG workflows.
- Familiarity with function calling, MCP, and designing or developing tools for AI applications.
- Familiarity with embeddings, vector search, prompt design, and document-processing pipelines.
- Experience with a relational database such as PostgreSQL and at least one vector database or vector-search solution.
- Frontend experience with JavaScript or TypeScript and a modern framework such as React.
- Familiarity with Git, automated testing, deployment workflows, and software engineering best practices.
- Strong problem-solving, communication, and collaboration skills.
Preferred Qualifications:
- Experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Familiarity with Docker, CI/CD, and production observability.
- Experience deploying LLMs on-premises, including using inference servers such as vLLM or SGLang.
- Experience with LLM evaluation, guardrails, hybrid search, reranking, or agentic workflows.
- Experience handling sensitive data and implementing security and privacy controls.
- Contributions to technical projects, open-source software, or developer communities.
Skills
Full Stack, Generative AI, Python, FastAPI, LLM, RAG, PostgreSQL, Django, Flask