Objectives
Develop complete applications, from reactive front-end architecture to resilient back-end services that orchestrate Artificial Intelligence workflows.
Responsibilities
- Systems Architecture: Design and implement modular and scalable architectures using Micro-frontends and Microservices to support mission-critical applications
- AI Engineering: Implement advanced integration patterns to consume and orchestrate AI models (OpenAI, Anthropic, Watsonx.ai, or Open Source models via Hugging Face)
- API Development: Build and maintain robust APIs (REST/GraphQL) that efficiently manage data processing and communication with AI services
- Context Management: Develop RAG (Retrieval-Augmented Generation) pipelines, integrating vector databases to provide relevant context to language models
- Performance & Security: Ensure client-side performance optimization (Core Web Vitals) and rigorous server-side security, especially when handling sensitive AI data
- Mentoring and Culture: Actively participate in Code Reviews, define engineering standards, and mentor junior team members, promoting a continuous learning environment
Technical Requirements and Profile
- Solid Experience: Minimum 5 years of proven experience developing complex applications using the React.js ecosystem (including Next.js and SSR) and backend languages like Node.js (TypeScript) or Python (FastAPI/Django)
- AI Ecosystem: Practical knowledge in LLM orchestration frameworks like LangChain, LlamaIndex, or Haystack. Experience with Prompt Engineering and model evaluation
- Databases: Mastery of relational databases (PostgreSQL/MySQL) and practical experience with Vector Databases (ChromaDB, Pinecone, Weaviate, or Milvus)
- Cloud & DevOps: Experience with Cloud providers (AWS, Azure, or IBM Cloud) and familiarity with Docker, Kubernetes, and CI/CD pipelines
- Architecture: Deep knowledge of software design patterns, event-oriented architecture, and complex state management (Zustand, Redux, or TanStack Query)
Soft Skills
- English fluency
- Natural "team player" who enjoys both learning new stacks and teaching what they know
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