Full Stack AI Engineer

Design and build full-stack AI features, including tables, document-centric interfaces, review flows, and real-time or streaming interactions. Develop agentic systems with tool calling, multi-step workflows, RAG, and structured output handling. Build backend services using ASP.NET Core Web API and Python (FastAPI), and integrate them with React/Next.js frontends. Implement and improve RAG pipelines, covering chunking, embedding selection, vector store integration, and retrieval quality evaluation. Design AI-native UX patterns: confidence indicators, citations and source grounding, fallback states, edit/retry flows, and human review steps. Write evaluation tests before shipping new AI capabilities, using golden datasets, regression gates, and CI controls. Contribute to evaluation pipelines that combine deterministic metrics with LLM-as-judge approaches. Build systems that degrade gracefully when model outputs are unexpected. Manage context windows through token budgeting, truncation, and tool-call state persistence. Prototype quickly with AI tooling, then validate production artifacts against defined quality gates before promotion. Requirements

Education and experience

Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience. 3+ years building production software, including full-stack applications and/or AI-enabled systems. Experience contributing to user-facing AI product features, from backend through frontend. Experience with agentic systems in production or pre-production (tool calling, multi-step workflows, RAG, structured outputs). Exposure to evaluation frameworks such as golden datasets, regression gates, or CI quality controls. Full-stack product engineering

Hands-on experience with.NET Core, ASP.NET Core Web API, SQL, and Microsoft technologies. Frontend skills in React and/or Next.js, TypeScript, component-based UI, API integration, and state management. Experience building tables, document-centric interfaces, review flows, or streaming experiences. Understanding of UX patterns for AI systems (confidence, citations, fallbacks, edit/retry, human review). AI platform engineering

Python proficiency, including FastAPI, Pydantic v2, async patterns, and pytest. Hands-on experience with LangChain and/or LangGraph: stateful graphs, tool integration, checkpointing, and streaming. Prompt engineering skills: structured output design, system prompt construction, and multi-turn context management. Experience with RAG pipelines and familiarity with vector databases such as pgvector and/or OpenSearch. Familiarity with LLM evaluation: golden dataset design, metric definition, and regression gates. Awareness of context window management strategies. Generative AI and agentic systems

Regular use of AI coding assistants (Cursor, GitHub Copilot), with good judgement about where generated code is reliable and where it needs scrutiny. Familiarity with multi-agent concepts: orchestration logic, tool interfaces, and failure-handling patterns.