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Senior Software Engineer (Agentic Platform), GreenNode
OfficialTechSoftware26-ENG-4028
Thành phố Hồ Chí Min...thành phố hồ chí minh
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About the role & product
We are building an **Agentic Platform** — a platform that helps customers deploy and operate AI agents at production scale, quickly and safely. The platform provides ready-made services / components so agent builders can integrate quickly instead of building from scratch — significantly shortening the time from idea to a finished agent. The platform also follows open protocols to easily connect with tools, data sources and other agents.
We believe a solid agentic platform is built on **foundational software engineering (backend & distributed systems)**, with AI / agents as the capability layer on top. Therefore, this role is designed with a **70% traditional software engineering / platform engineering** and **30% AI, AI agent and related protocols** split.
You will work directly with the engineering team to take the services powering the platform from architecture, implementation, to operation — ensuring high availability, low latency, security and scalability.
Main responsibilities
1. Platform & Backend Engineering (~70%)
- Design, implement and maintain backend services / APIs that meet high standards of **availability, latency and security**.
- Build the platform's infrastructure components to support the agent lifecycle — from where agents run, communication between agent / tool / service, state management, to security and authorization.
- Build a **secure sandbox runtime** where AI agents can execute generated code and tools in isolation — using containerization / microVMs (Docker, gVisor, Firecracker), namespace & seccomp isolation, resource limits (CPU / memory / network), and strict egress controls so untrusted agent actions never leak into the host or other tenants.
- Design data models and data flows on SQL (MySQL), NoSQL (MongoDB) and vector databases; ensure consistency, throughput and recoverability.
- Integrate message brokers / event-driven backbones (Kafka, RabbitMQ, AWS SQS/SNS) for asynchronous communication between the platform's internal services.
- Identify and resolve system issues: performance bottlenecks, memory leaks, race conditions, security vulnerabilities, resource leaks.
- Design and deploy **observability** systems (metrics, logs, distributed tracing) to monitor and debug the platform in production.
- Research, experiment with and evaluate new technology solutions to solve the system's technical problems.
2. AI & Agentic Capability (~30%)
- Design foundational services for AI agents; proactively keep up with trending AI agent features in the market so customers always access the latest capabilities.
- Build and integrate **MCP servers** as well as adapters for the **A2A** protocol to connect agents with tools, data sources and other agents.
- Integrate LLMs / agentic frameworks (LangChain, LangGraph, CrewAI, Strands…) into the platform in a framework-agnostic way; design abstractions so the platform does not depend on any specific model or framework.
- Design and build the **agent sandbox capability** — a code / tool execution environment (code interpreter, REPL, tool-call runtime) that gives AI agents the ability to run code, evaluate outputs and iterate safely; support multi-language execution, persistable session state, file I/O, and deterministic reproducibility so agents can reliably "think → execute → observe → refine".
- Define and enforce **sandbox security boundaries** — permission scoping per agent / per task (read-only fs, network allow-lists, approval gates for sensitive actions), and deep integration with the platform's authorization layer so sandbox actions are auditable, traceable and revocable.
- Write clients (SDK, CLI, AI agent skills) to help customers integrate and use the platform.
- Apply AI tooling (coding assistant, AI code review, AI-assisted testing & debugging) into the daily development and operations workflow to boost team productivity.
We are building an **Agentic Platform** — a platform that helps customers deploy and operate AI agents at production scale, quickly and safely. The platform provides ready-made services / components so agent builders can integrate quickly instead of building from scratch — significantly shortening the time from idea to a finished agent. The platform also follows open protocols to easily connect with tools, data sources and other agents.
We believe a solid agentic platform is built on **foundational software engineering (backend & distributed systems)**, with AI / agents as the capability layer on top. Therefore, this role is designed with a **70% traditional software engineering / platform engineering** and **30% AI, AI agent and related protocols** split.
You will work directly with the engineering team to take the services powering the platform from architecture, implementation, to operation — ensuring high availability, low latency, security and scalability.
Main responsibilities
1. Platform & Backend Engineering (~70%)
- Design, implement and maintain backend services / APIs that meet high standards of **availability, latency and security**.
- Build the platform's infrastructure components to support the agent lifecycle — from where agents run, communication between agent / tool / service, state management, to security and authorization.
- Build a **secure sandbox runtime** where AI agents can execute generated code and tools in isolation — using containerization / microVMs (Docker, gVisor, Firecracker), namespace & seccomp isolation, resource limits (CPU / memory / network), and strict egress controls so untrusted agent actions never leak into the host or other tenants.
- Design data models and data flows on SQL (MySQL), NoSQL (MongoDB) and vector databases; ensure consistency, throughput and recoverability.
- Integrate message brokers / event-driven backbones (Kafka, RabbitMQ, AWS SQS/SNS) for asynchronous communication between the platform's internal services.
- Identify and resolve system issues: performance bottlenecks, memory leaks, race conditions, security vulnerabilities, resource leaks.
- Design and deploy **observability** systems (metrics, logs, distributed tracing) to monitor and debug the platform in production.
- Research, experiment with and evaluate new technology solutions to solve the system's technical problems.
2. AI & Agentic Capability (~30%)
- Design foundational services for AI agents; proactively keep up with trending AI agent features in the market so customers always access the latest capabilities.
- Build and integrate **MCP servers** as well as adapters for the **A2A** protocol to connect agents with tools, data sources and other agents.
- Integrate LLMs / agentic frameworks (LangChain, LangGraph, CrewAI, Strands…) into the platform in a framework-agnostic way; design abstractions so the platform does not depend on any specific model or framework.
- Design and build the **agent sandbox capability** — a code / tool execution environment (code interpreter, REPL, tool-call runtime) that gives AI agents the ability to run code, evaluate outputs and iterate safely; support multi-language execution, persistable session state, file I/O, and deterministic reproducibility so agents can reliably "think → execute → observe → refine".
- Define and enforce **sandbox security boundaries** — permission scoping per agent / per task (read-only fs, network allow-lists, approval gates for sensitive actions), and deep integration with the platform's authorization layer so sandbox actions are auditable, traceable and revocable.
- Write clients (SDK, CLI, AI agent skills) to help customers integrate and use the platform.
- Apply AI tooling (coding assistant, AI code review, AI-assisted testing & debugging) into the daily development and operations workflow to boost team productivity.
3.Engineering Practice & Ownership (applies to both parts)
- Write clean code with unit and integration tests; maintain quality through code review.
- Own features / services from design to production; proactively propose architecture improvements.
- Mentorship and review for junior / mid engineers; contribute to building engineering culture.
- Work closely with product, infra and stakeholders to turn requirements into clear technical solutions.
- Write clean code with unit and integration tests; maintain quality through code review.
- Own features / services from design to production; proactively propose architecture improvements.
- Mentorship and review for junior / mid engineers; contribute to building engineering culture.
- Work closely with product, infra and stakeholders to turn requirements into clear technical solutions.
Yêu cầu
Job requirements
I/ Must-have
1. Background & experience
- Bachelor's degree or above in Computer Science, Engineering or a related field.
- Experience building backend services / distributed systems running in production; for us, years of experience is only a reference number — **actual capability** is the deciding factor.
- Solid foundation in **software architecture, design patterns, distributed systems** and best practices.
- Strong problem-solving, logical thinking and analytical skills; effective communication and collaboration.
2. Backend & Data
- Proficient in at least one of: **Java (Spring Boot), Go, Python**.
- Proficient in **SQL (MySQL)** and **NoSQL (MongoDB)**; understand indexing, query optimization, transactions and data modeling.
- Hands-on experience with message brokers: **Kafka, RabbitMQ, ActiveMQ** or **AWS SQS/SNS**.
- Good understanding of **REST API** design, authentication / authorization (OAuth, API key, service-to-service auth) and security fundamentals.
3.Infra & DevOps
- Familiar with **Git, Docker, CI/CD**; understand microservices deployment.
- Understand **observability** (metrics, logs, tracing) and have worked with related tools (Prometheus/Grafana, ELK/OpenSearch, OpenTelemetry, Jaeger…).
4. AI & Agentic
- Understand the core concepts of agent architecture: **tool use, memory/context, orchestration loop, guardrails, multi-agent coordination**.
- Understand and have worked with **MCP (Model Context Protocol)** — **knowing how to write an MCP server is a big plus**.
- Understand **A2A (Agent-to-Agent)** and other agent protocols (ADK, ReAct).
- Have used agentic frameworks (LangChain, LangGraph, CrewAI…) and/or RAG / vector databases.
- Familiar with using AI tooling in daily work.
II/ Nice-to-have
- Have built or contributed to a real **agentic platform / AI platform**.
- Have written an **MCP server** and integrated it into a production system.
- Have deployed an end-to-end **monitoring / observability** system.
- Experience with **Kubernetes** and operating container workloads at scale.
- Experience with **code interpreter sandbox**, identity / authorization for agents, or semantic caching.
- Have built a **secure sandbox / code execution runtime** for AI agents or multi-tenant workloads — hands-on with container isolation (Docker, gVisor, Firecracker / microVMs), namespace & seccomp, network policies, or serverless code execution platforms.
- **Good English listening and speaking skills** — a plus for working with international teams, partners and technical documentation.
I/ Must-have
1. Background & experience
- Bachelor's degree or above in Computer Science, Engineering or a related field.
- Experience building backend services / distributed systems running in production; for us, years of experience is only a reference number — **actual capability** is the deciding factor.
- Solid foundation in **software architecture, design patterns, distributed systems** and best practices.
- Strong problem-solving, logical thinking and analytical skills; effective communication and collaboration.
2. Backend & Data
- Proficient in at least one of: **Java (Spring Boot), Go, Python**.
- Proficient in **SQL (MySQL)** and **NoSQL (MongoDB)**; understand indexing, query optimization, transactions and data modeling.
- Hands-on experience with message brokers: **Kafka, RabbitMQ, ActiveMQ** or **AWS SQS/SNS**.
- Good understanding of **REST API** design, authentication / authorization (OAuth, API key, service-to-service auth) and security fundamentals.
3.Infra & DevOps
- Familiar with **Git, Docker, CI/CD**; understand microservices deployment.
- Understand **observability** (metrics, logs, tracing) and have worked with related tools (Prometheus/Grafana, ELK/OpenSearch, OpenTelemetry, Jaeger…).
4. AI & Agentic
- Understand the core concepts of agent architecture: **tool use, memory/context, orchestration loop, guardrails, multi-agent coordination**.
- Understand and have worked with **MCP (Model Context Protocol)** — **knowing how to write an MCP server is a big plus**.
- Understand **A2A (Agent-to-Agent)** and other agent protocols (ADK, ReAct).
- Have used agentic frameworks (LangChain, LangGraph, CrewAI…) and/or RAG / vector databases.
- Familiar with using AI tooling in daily work.
II/ Nice-to-have
- Have built or contributed to a real **agentic platform / AI platform**.
- Have written an **MCP server** and integrated it into a production system.
- Have deployed an end-to-end **monitoring / observability** system.
- Experience with **Kubernetes** and operating container workloads at scale.
- Experience with **code interpreter sandbox**, identity / authorization for agents, or semantic caching.
- Have built a **secure sandbox / code execution runtime** for AI agents or multi-tenant workloads — hands-on with container isolation (Docker, gVisor, Firecracker / microVMs), namespace & seccomp, network policies, or serverless code execution platforms.
- **Good English listening and speaking skills** — a plus for working with international teams, partners and technical documentation.
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