Job Description

Zalo Business Solution (ZBS) are powerful business solutions that enable enterprises to connect with customers through Zalo Official Accounts and send secure, personalized notifications, enhancing engagement and communication efficiency at a scale of tens of millions of users.

We are looking for a (Lead) Senior Software Engineer to spearhead our DevOps team. In this role, engineering excellence comes first. You will be responsible for architecting and building high-performance, mission-critical backend systems that integrate cutting-edge AI capabilities. You are someone who believes that AI is only as good as the system it runs on. Your mission is to lead a team in transforming ZBS into an AI-driven platform by applying rigorous software engineering principles to AI integration.

What you will do

System Architecture: Lead the design and development of high-performance, scalable backend services using Java (Spring Boot) that serve as the backbone for AI features;
AI Integration & Orchestration: Architect how AI models (LLMs, Agents) are integrated into the ZBS microservices ecosystem, focusing on reliability, low latency, and efficient resource management;
Lead Innovation Workflows: Build internal developer platforms and AI-powered automation toolkits to optimize the entire engineering organization's lifecycle;
Production-Grade AI: Move AI prototypes from "it works on my machine" to "it works for millions of users." This includes owning the billing, monitoring, and scaling logic of AI services;
Engineering Leadership: Set the bar for code quality, system design, and technical documentation. Mentor engineers on how to build robust software that leverages AI without sacrificing stability;
Strategic AI Implementation: Collaborate with business leads to identify where AI can solve complex engineering bottlenecks or create new product value, and then execute the technical roadmap.

What you will need

Software Mastery: 5+ years of experience in Backend Engineering, with expert-level proficiency in Java and the Spring ecosystem;
Systems Thinking: Deep understanding of distributed systems, microservices architecture, concurrency, and high-traffic platform engineering;
AI-Enhanced Engineering: Hands-on experience with AI tools (Cursor, Claude, etc.) and a proven track record of using them to accelerate software delivery and solve complex bugs;
Practical AI Expertise: Solid grasp of LLM integration, RAG architectures, and Agentic workflows (Python/LangGraph/LangChain) with the ability to bridge these into a Java-based production environment;
Infrastructure Excellence: Strong experience with Linux, Docker, Kubernetes, and building sophisticated CI/CD pipelines that handle both code and model deployments;
Data & Performance: Proficiency in SQL/NoSQL, Redis, and experience with data systems like Kafka, Apache Spark, or Apache Doris for processing high-volume event streams;
Ownership Mindset: Experience owning systems end-to-end from the first line of code to production monitoring and incident response.

Nice to have

AI Observability & Evaluation Frameworks: Experience building automated evaluation pipelines for LLMs (using RAGAS, DeepEval, or Arize Phoenix) and implementing specialized monitoring for "AI health" (hallucination rates, token-per-second, and context window efficiency);
Self-hosting & Model Adaptation: Proven track record of self-hosting and fine-tuning state-of-the-art open-source models (DeepSeek, Llama 3, Mistral) on private infrastructure to ensure data privacy and reduce dependency on external APIs;
Observability & Security: Hands-on with Prometheus, Grafana, and ELK; strong awareness of OWASP Top 10 and data privacy.