Senior Python & AI Developer

Jobtailor; São Paulo; Presencial; BRL 120 000 - 160 000; Tempo integral

Resumo da oferta

Jobtailor is seeking a skilled AI engineer to develop LLM-based applications and services using Python. You will design and implement RAG solutions, index knowledge with vector databases, and build agent architectures and multi-agent systems.

Expect Pipelines with LangChain/LangGraph, observability with LangFuse, and integration across APIs and microservices. You will collaborate with cross-functional teams, ensuring quality, scalability, and compliance with industry standards while leveraging

Qualificações

• Develop LLM-based applications and services using Python.
• Design and implement RAG (Retrieval-Augmented Generation) solutions.
• Work with vector databases for knowledge indexing and retrieval.
• Develop agent-based architectures and multi-agent systems.
• Build pipelines and applications with LangChain and LangGraph.
• Implement observability for AI applications using tools such as LangFuse.
• Integrate AI solutions with APIs, microservices, and existing systems.
• Python proficiency required.

Responsabilidades

• Develop LLM-based applications and services using Python.
• Design and implement RAG solutions and knowledge indexing.
• Build pipelines and applications with LangChain and LangGraph.
• Integrate AI solutions with APIs and microservices with robust quality testing.

Conhecimentos

Python

RAG (Retrieval-Augmented Generation)

LangChain

LangGraph

Agent Architectures

Multi-Agent Systems

Vector Databases

REST APIs

Microservices

Quality Testing of LLM Outputs

Git

Agile Methodologies

Formação académica

Completed Higher Education Degree

Ferramentas

LangFuse

Pinecone

Weaviate

Qdrant

Chroma

Descrição da oferta de emprego

• Develop LLM-based applications and services using Python


• Design and implement RAG solutions


• Index and retrieve knowledge using vector databases


• Develop agent architectures and multi-agent systems


• Build pipelines and applications with LangChain and LangGraph


• Implement observability for AI applications using LangFuse or similar tools


• Integrate AI solutions with APIs, microservices, and existing systems


• Collaborate with cross-functional teams to transform business needs into AI solutions


• Ensure quality, scalability, and adherence to development best practices


• Participate in the active Talent Pool and confirm availability every 90 days

Requirements

• Develop LLM-based applications and services using Python


• Design and implement RAG (Retrieval-Augmented Generation) solutions


• Work with vector databases for knowledge indexing and retrieval


• Develop agent-based architectures and multi-agent systems


• Build pipelines and applications with LangChain and LangGraph


• Implement observability for AI applications using tools such as LangFuse


• Integrate AI solutions with APIs, microservices, and existing systems


• Python


• LangChain and LangGraph


• RAG (Retrieval-Augmented Generation)


• Agent architectures and multi-agent systems


• Experience with Pinecone, Weaviate, Qdrant, Chroma, or similar technologies


• Chunking, embeddings, and reranking strategies


• LangFuse or similar tools


• REST APIs and microservices integration


• Git and agile methodologies (Scrum, Kanban)


• Completed higher education degree


• Ability to communicate AI model limitations to non-technical audiences


• Experience in the banking, financial services, or insurance sectors is a plus


• Experience evaluating and quality-testing LLM outputs is a plus


• Experience with fine-tuning, model serving, or open-source models is a plus


• Knowledge of AWS, Azure, or GCP and AI application deployment is a plus


• Experience handling sensitive data, LGPD, and compliance requirements is a plus

Core Competencies

Demonstrates expertise in developing LLM-based applications using Python, designing RAG solutions, and integrating AI solutions with APIs and microservices. Proficient in building applications with LangChain and LangGraph while ensuring quality and scalability in AI deployments.

Highest-signal resume keywords

• LLM-Based Application Development


• RAG (Retrieval-Augmented Generation) Solutions


• LangChain and LangGraph


• Vector Database Experience


• AI Application Observability

ATS Optimization Keywords

Hard Skills

• Python


• RAG (Retrieval-Augmented Generation)


• Agent Architectures


• Multi-Agent Systems


• Chunking


• Embeddings


• Reranking Strategies


• REST APIs


• Microservices Integration


• Quality Testing of LLM Outputs

Soft Skills

• Communication of AI Model Limitations

Certifications & Qualifications

• Completed Higher Education Degree

Industry Keywords

• Banking


• Financial Services


• Insurance


• Sensitive Data Handling


• LGPD Compliance

Tools & Technologies

• LangFuse


• Pinecone


• Weaviate


• Qdrant


• Chroma


• Git


• Agile Methodologies