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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing AI agents and LLM-based workflows using Python, LangChain, and LangGraph, while integrating with APIs and enterprise systems. Proficient in optimizing AI solutions for performance, cost, and compliance with data privacy principles.
Highest-signal resume keywords
Python DevelopmentLangChain and LangGraphRAG PipelinesAPI IntegrationLLM Evaluation Tools
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonLLMsLangChainLangGraphRAGFastAPIFlaskDockerGitCloud Platforms
Tools & Technologies
OpenAIAnthropic ClaudeAzure OpenAIPgvectorPineconeChromaFAISSLangSmithLangfuseN8n
Industry Keywords
AI AutomationData PrivacySecurity PrinciplesMCPToken Management
Tech Stack
Tools & technologiesAirflowAWSAzureDockerFlaskGoogle Cloud PlatformOpen SourcePythonRPA
About the role
Key responsibilities & impact- Develop AI agents and LLM-based workflows in Python using LangChain and LangGraph
- Map business processes and identify opportunities for AI-powered automation
- Implement RAG pipelines over knowledge bases and internal documents
- Integrate agents with the company’s systems, APIs, and databases
- Evaluate, monitor, and improve response quality, latency, and solution costs
- Deploy solutions to production with testing, version control, and documentation
Requirements
What you’ll need- Experience developing in Python, including APIs, automation, and integrations
- Hands-on experience with LLMs, including OpenAI, Anthropic Claude, Azure OpenAI, Gemini, or open-source models, as well as prompt engineering
- Experience developing AI agents with LangChain and LangGraph, including tool calling, memory, multi-step workflows, and multi-agent orchestration
- RAG (Retrieval-Augmented Generation), including embeddings, chunking, and vector databases such as pgvector, Pinecone, Chroma, FAISS, or similar technologies
- Experience integrating REST APIs and enterprise systems to automate end-to-end processes
- Experience building services with FastAPI or Flask, Docker, and Git with CI/CD
- Experience with at least one cloud platform, such as AWS, Azure, or GCP, for deploying AI solutions
- Experience with LLM evaluation and observability tools, such as LangSmith, Langfuse, RAGAS, or similar technologies, as well as guardrails
- Experience with MCP (Model Context Protocol) and integrating agents with external tools
- Familiarity with automation and orchestration tools, such as n8n, Airflow, and RPA
- Experience optimizing LLM costs and latency through caching, model selection, and token management
- Understanding of security and data privacy principles applied to AI, including Brazil’s LGPD and sensitive data
Benefits
Comp & perks- None explicitly stated