Full Stack AI Engineer

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 workows.
Design and develop tools for AI applications, including function-calling workows
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 Qualications
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 workows.
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 workows, and software
engineering best practices.
Strong problem-solving, communication, and collaboration skills.
Preferred Qualications
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
workows.
Experience handling sensitive data and implementing security and privacy controls.
Contributions to technical projects, open-source software, or developer
communities.