Build agents using established frameworks like LangChain and LangGraph to manage complex, multi-step workflows with failure recovery and state management.
Connect agents to professional toolsets (CRM, Issue Trackers, and Communication platforms) via APIs, OAuth2, and webhooks.
Implement and tune retrieval systems using vector databases (qdrant, pgvector) for high-accuracy similarity search and duplicate detection.
Use Vision-based LLMs to extract structured data from unstructured documents.
Implement workflow automations using approved tools (n8n, Zapier, internal platforms).
Instrument agents with tools like LangSmith for tracing, performance benchmarking, and cost tracking.

Requirements

Experience with Python or TypeScript through coursework, projects, or internships
Basic understanding of LLM concepts (prompting, context windows, tool calling)
Hands-on use of GenAI tools (OpenAI, Claude, Gemini, etc.)
Comfort working with APIs, JSON, and webhooks
Willingness to follow existing frameworks instead of reinventing them
Familiarity with Retrieval-Augmented Generation (RAG) architectures and how context is retrieved, grounded, and passed to LLMs

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

PythonTypeScriptLLM conceptsGenAI toolsAPIsJSONwebhooksRetrieval-Augmented Generationvector databasesworkflow automation

problem-solvingadaptabilitycollaborationattention to detailwillingness to follow frameworks