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.