Senior RTL Design Engineer
ABOUT CLIENT
JOB DESCRIPTION
JOB REQUIREMENT
WHAT'S ON OFFER
CONTACT
Job Summary
Company Type:
Product
Technical Skills:
RTL, Hardware
Location:
Ho Chi Minh - Viet Nam
Working Policy:
Hybrid
Job ID:
J01602
Status:
Close
Related Job:
Software Engineer (Node.js) - Database
Ho Chi Minh - Viet Nam
Product
- NodeJS
Design system architectures, establish coding standards, and construct cohesive, cloud-native solutions. Develop high-quality Node.js code, optimize system performance, and tackle complex software integration challenges. Oversee the testing, deployment, and comprehensive documentation of integrated systems. Mentor less-experienced engineers, engage in cross-functional teamwork, and ensure solutions meet business requirements and international standards. Participate actively in all Agile software development phases, including creating user stories and executing sprint planning Engage with multinational companies, demonstrating flexibility to occasionally adapt to US and EU time zones.
Negotiation
View detailsSoftware Engineer (Node.js) - Platform Security
Ho Chi Minh - Viet Nam
Product
- NodeJS
Design system architectures, establish coding standards, and construct cohesive, cloud-native solutions. Develop high-quality Node.js code, strengthen system security and reliability, and tackle complex software integration challenges. Design and implement platform security controls across web applications, APIs, and cloud services, including authentication, authorization, session management, secrets management, encryption, and audit logging. Identify and remediate security risks through threat modeling, secure code reviews, automated security testing, dependency scanning, and investigation of security-related issues. Oversee the testing, deployment, and comprehensive documentation of integrated systems. Mentor less-experienced engineers, engage in cross-functional teamwork, and ensure solutions meet business requirements and international standards. Participate actively in all Agile software development phases, including creating user stories and executing sprint planning Engage with multinational companies, demonstrating flexibility to occasionally adapt to US and EU time zones.
Negotiation
View detailsAI Agent Ops Engineer
Ho Chi Minh - Viet Nam
Product
- AI
#Agent Engineering & operation Design, build, and maintain production-grade AI agent systems, including: context engineering and instruction architecture, prompt hardening and safe execution boundaries, tool integrations and multi-step orchestration, memory strategies and reliability patterns. Own the full agent lifecycle: prototype → evaluate → deploy → monitor → iterate. Build and maintain an evaluation pipeline to measure agent quality, catch regressions, and enforce deployment gates (golden datasets, scenario suites, automated checks). Instrument agents and agent platforms for production observability: structured logging, tracing, and metrics; latency and cost monitoring; tool-call success rates and failure analysis. Define operational readiness standards including: rollback criteria, incident response playbooks, recovery paths for common failure modes.#Team Enablement & Coaching Embed with product engineering teams to identify high-value use cases ready for agent automation. We will be operating in a Central Agent Ops role enabling Ai product builders through AI enablers. Translate business workflows into agent-executable tasks with clear: contact boundaries/interfaces, assumptions and inputs/outputs, failure modes and safe fallbacks. Deliver targeted coaching to engineers on: context engineering best practices, harness design and regression testing patterns, agent skill design and tool-contract discipline. Reduce onboarding time for teams adopting AI capabilities-from first conversation to a production-ready agent. Train product engineers to extend and maintain agent skills independently.#Standards & Knowledge operations Author and maintain org-level standards for agents, including: naming conventions, context file structures and ownership rules, skill interface contracts (inputs/outputs, invariants, error handling), evaluation criteria and release quality bars. Establish and enforce "repo-as-discipline" practices so agent knowledge is: versioned, reviewable, discoverable, reusable; not trapped in prompt snippets or individual heads. Build and grow a shared agent skills library that teams can reuse and extend. Track and aggregate AI tooling/framework updates and external best practices, serving as a central intake so product teams don't each have to follow the entire AI landscape. Run internal knowledge-sharing sessions, showcases, and retrospectives to propagate learnings efficiently.