What you will do:
Build and ship APIs in Python (FastAPI) or .NET/C# with tests, type hints, and clean structure.
Contribute to agentic features using Semantic Kernel or LangChain/LangGraph (tool calling + structured outputs).
Implement simple RAG (1-liner): wire retrieval using Azure AI Search or a vector store (e.g., pgvector) under mentorship.
Instrument & iterate: add traces/metrics (OTEL, Langfuse), watch latency/cost, write concise docs, and demo your work.
Minimum qualifications
Recent graduate (0–1 year experience).
Strong data structures & algorithms (arrays, strings, hash maps, graphs, complexity).
Proficient in Python or C#, Git, and basic SQL.
Side/college projects in AI (share links!): e.g., chatbot, RAG , classifier, or agent using tools/functions.
Familiar with prompts, tool calling, structured outputs; eager to learn multi-provider LLM patterns.
Clear written/spoken communication; comfortable with 8:30 AM–12:30 PM ET overlap.
Nice to have
Vector DB basics (pgvector/Qdrant/Chroma) and Azure fundamentals.
Containers (Docker), Postgres basics, Langfuse/OpenTelemetry exposure.
Any experience with LangChain/LangGraph or Semantic Kernel.
What you’ll learn here
Agent orchestration patterns in production (handoff / sequential / concurrent).
RAG evaluation basics and index hygiene.
CI/CD and code review habits for high-throughput services.
How to design for reliability, cost, and latency in GenAI systems.