**Job overview:**

Build and operate scalable backend and AI platforms, with Python as the primary language. The role owns production Retrieval\-Augmented Generation (RAG) and agentic capabilities across APIs, data, infrastructure and user\-facing workflows, while welcoming strong engineers with relevant experience in Go, Java, Node.js or other modern technology stacks.

**Key Responsibilities:**

Build scalable platforms: Develop and maintain backend and AI\-platform services, primarily in Python; use Go, Java, Node.js or other suitable technologies when they improve performance or reliability. Own production RAG pipelines: Deliver document ingestion and processing, chunking, embeddings, vector/hybrid search, retrieval and reranking, context management, prompt optimization and quality evaluation.

• Develop agentic AI capabilities: Build multi\-agent orchestration, MCP, A2A, tool integration, state/task coordination, and integrations with LLM providers such as OpenAI and Anthropic.
• Design end\-to\-end services: Create REST/GraphQL APIs, microservices and event\-driven workflows; integrate SQL, NoSQL, vector databases and third\-party services.
• Optimize scale and performance: Improve concurrency, asynchronous processing, caching, Kafka\-based messaging, observability and large\-scale data processing.
• Ship production\-ready systems: Apply automated testing, code review, documentation, security practices, CI/CD, Docker/Kubernetes, cloud and infrastructure as code.
• Collaborate and lead: Partner with AI/ML, Product, Design and Frontend teams; mentor engineers and evaluate emerging AI technologies.

**Requirements:**

Experience and education: Bachelor's degree in Computer Science, Software Engineering or a related field, with 5\+ years of backend or full\-stack software development experience. Python first, broader stack welcome: Strong production Python and clean\-code skills; experience with one or more of Go, Java, Node.js or comparable backend languages; sound knowledge of concurrency, threading and event loops.

• Backend architecture: Hands\-on experience with FastAPI, Flask or equivalent frameworks; REST/GraphQL, microservices, event\-driven architecture, system design, SOLID principles and design patterns.
• RAG, LLMs and agentic systems: Hands\-on delivery of RAG/LLM systems covering document processing, embeddings, semantic/vector/hybrid search, retrieval, context and prompt engineering, LLM\-provider integration, multi\-agent orchestration and MCP/A2A; experience with LangChain, LlamaIndex, AutoGen or CrewAI is valued, and 2\+ years in AI platforms, RAG or agents is preferred.
• Data and messaging: PostgreSQL/MySQL, Redis/MongoDB, vector stores such as Qdrant, Pinecone, Weaviate or Milvus, plus Kafka and asynchronous processing.
• Delivery and cloud: Git, Docker, Kubernetes and CI/CD; AWS/GCP/Azure and Terraform/CloudFormation experience are preferred.
• Quality and collaboration: Unit/integration/edge\-case testing, OWASP, rate limiting, logging, token management, monitoring/observability, debugging and performance optimization; strong communication and mentoring. React or Vue.js experience is a plus.

**Contact:**

Ms. Nguyệt Zalo/Call: 0769 288 088Email: [email protected]

Office: Hanoi: Technopark, Gia Lam