Conversational Agent Development: Build and evolve AI agents for customer service, negotiation, and conversion across channels such as WhatsApp, voice, and webchat.
LLM Orchestration: Implement flows using frameworks like LangGraph (or similar), managing context, memory, tools (tool calling), and conversation states.
Prompt Engineering: Create, test, and optimize prompts for different scenarios (collections, sales, support), ensuring consistency and performance.
Tool Integration: Connect agents to APIs and external systems (e.g., Pix payment generation, data lookup, CRM), enabling in-conversation actions.
AI Quality Control: Monitor responses, prevent hallucinations, and ensure adherence to business rules.
Personalization: Work with contextual data (customer profile, history, segmentation) to make interactions smarter and more effective.
Experimentation: Run A/B tests on approaches, flows, and prompts, continuously improving agent performance.
AI Observability: Track usage, cost, latency, and response quality metrics (LLM monitoring).
Cost Optimization: Implement strategies to reduce cost per interaction (model selection, caching, fallbacks, etc.).
Collaboration: Work closely with backend, product, and operations teams to ensure AI delivers real business impact.

Experience with LLMs: Hands-on experience using models such as GPT, Claude, or similar.
Prompt Engineering: Ability to structure efficient, predictable prompts for various scenarios.
Agent Orchestration: Experience with frameworks like LangChain, LangGraph, or similar.
APIs: Experience integrating with APIs and using tool calling.
Programming: Strong backend development skills (preferably Node.js or Python).
Context Management: Experience with conversation memory, state, and flow control.
AI Debugging: Ability to analyze and correct unexpected model behaviors.
Versioning: Experience with Git.

AI Engineer

Key skills