Toptal: Python Backend Development Talent with RAG and Agentic AI Experience

Mosaec; Asia; Presencial; PEN 404.000 - 607.000; Jornada completa

Descripción de la vacante

Mosaec in Peru is seeking an experienced Python Backend Developer to design, build, and deploy scalable AI-powered applications using RAG, LLMs, and agentic AI frameworks.

You will own the architecture, build REST APIs, microservices, and production-grade pipelines, coordinating with a distributed team that overlaps with US hours.

Formación

• 8+ years of professional Python backend development experience.
• Design REST APIs, microservices, asynchronous services, and distributed backend systems.
• Hands-on experience building production-grade RAG applications.
• Deep understanding of embeddings, document chunking, semantic search, vector indexing, retrieval strategies, and reranking.
• Hands-on experience developing AI agents and multi-step LLM workflows.
• Experience with agentic AI frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or comparable platforms.
• Experience integrating LLMs via OpenAI, Azure OpenAI, Anthropic Claude, Gemini, or open-source model APIs.
• Ability to design AI architecture beyond basic prompt engineering.
• Experience integrating AI applications with APIs, databases, data pipelines, and enterprise systems.
• Experience implementing security, monitoring, logging, tracing, and observability for production services.
• Experience deploying containerized applications using Docker and cloud platforms such as Azure or AWS.
• Ability to translate business requirements into scalable technical solutions.
• Strong communication and collaboration skills in a distributed working environment.
• Availability for several hours of overlap with US working hours.

Responsabilidades

• Design end-to-end architecture for scalable RAG system and AI chatbot.
• Develop Python backend services, REST APIs, and microservices using FastAPI or similar frameworks.
• Build document ingestion, chunking, embedding, indexing, retrieval, and reranking pipelines.
• Implement vector search solutions using Azure AI Search or comparable vector databases.
• Design autonomous AI agents with planning, reasoning, tool usage, and decision-making.
• Develop multi-step AI workflows using LangGraph, LangChain, CrewAI, AutoGen, or similar orchestration frameworks.
• Integrate LLMs (OpenAI, Azure OpenAI, Claude, Gemini) and manage model APIs.
• Implement conversation memory, session/context management, and agent collaboration patterns.
• Connect AI workflows with APIs, databases, enterprise systems, and external tools.
• Containerize and deploy AI services using Docker and cloud platforms (Azure/AWS).
• Translate business requirements into technical designs and milestones.
• Collaborate with the wider team while independently owning architecture decisions.

Conocimientos

Python backend development

REST APIs

Microservices

Asynchronous services

Production-grade RAG apps

Embeddings and vector search

AI agents & LLM workflows

LangGraph/LangChain/CrewAI/AutoGen

OpenAI/Azure OpenAI/Claude/Gemini

API/integration with enterprise data

Docker/Kubernetes deployment

Cloud platforms (Azure/AWS)

Distributed collaboration

Herramientas

LangGraph

LangChain

CrewAI

AutoGen

Azure OpenAI

OpenAI

Anthropic Claude

Gemini

Descripción del empleo

Headquarters:

Summary:

We are seeking an experienced Python Backend Developer to design, build, and deploy scalable AI-powered applications using Retrieval-Augmented Generation, large language models, and agentic AI frameworks. The role will focus on delivering a production-grade RAG system and AI chatbot that can securely integrate with enterprise data, APIs, databases, and cloud services.

General information:

The organization is developing an AI-powered platform and requires an experienced individual contributor to build its RAG architecture and conversational AI capabilities. The developer will work closely with a distributed team and should be available for several hours of overlap with US working hours.

The project involves designing AI systems that go beyond basic prompt engineering, including multi-step workflows, autonomous agents, vector search, knowledge retrieval, memory management, and tool integration. The solution must be scalable, secure, observable, and suitable for production use.

The technology environment includes Python, FastAPI, large language models, LangGraph, LangChain, vector databases, Azure AI services, AWS, Docker, Kubernetes, and microservice-based architectures.

Task and deliverables:

• Design the end-to-end architecture for a scalable RAG system and AI chatbot.
• Develop Python backend services, REST APIs, and microservices using FastAPI or similar frameworks.
• Build document ingestion, chunking, embedding, indexing, retrieval, and reranking pipelines.
• Implement vector search solutions using Azure AI Search or comparable vector databases.
• Design autonomous AI agents capable of planning, reasoning, tool usage, and decision-making.
• Develop multi-step AI workflows using LangGraph, LangChain, CrewAI, AutoGen, or similar orchestration frameworks.
• Integrate commercial and open-source LLMs, including Azure OpenAI, OpenAI, Anthropic Claude, Gemini, and comparable models.
• Implement conversation memory, session management, context management, and agent collaboration patterns.
• Connect AI workflows with APIs, databases, enterprise systems, and external tools.
• Develop asynchronous, high-performance services capable of handling concurrent AI workloads.
• Implement prompt management, structured outputs, guardrails, fallback logic, and model evaluation processes.
• Establish logging, monitoring, tracing, observability, security, and error-handling standards.
• Containerize and deploy AI services using Docker, Kubernetes, Azure, or AWS.
• Translate business requirements into technical designs, delivery milestones, and production-ready AI solutions.
• Collaborate with the wider team while independently owning architecture and implementation decisions.

Required experience:

• Required: 8 or more years of professional Python backend development experience.
• Required: Strong experience designing REST APIs, microservices, asynchronous services, and distributed backend systems.
• Required: Hands-on experience building production-grade RAG applications.
• Required: Strong understanding of embeddings, document chunking, semantic search, vector indexing, retrieval strategies, and reranking.
• Required: Hands-on experience developing AI agents and multi-step LLM workflows.
• Required: Experience with agentic AI frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or comparable platforms.
• Required: Experience integrating LLMs through OpenAI, Azure OpenAI, Anthropic Claude, Gemini, or open-source model APIs.
• Required: Ability to design AI architecture beyond basic prompt engineering.
• Required: Experience integrating AI applications with APIs, databases, data pipelines, and enterprise systems.
• Required: Experience implementing security, monitoring, logging, tracing, and observability for production services.
• Required: Experience deploying containerized applications using Docker and cloud platforms such as Azure or AWS.
• Required: Ability to independently translate business requirements into scalable technical solutions.
• Required: Strong communication and collaboration skills in a distributed working environment.
• Required: Availability for several hours of overlap with US working hours.