• 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.