Data Engineer

JOB DESCRIPTION

Work with fellow engineers and data scientists to develop and maintain our core product.
Implement our data pipelines (Java, Elasticsearch, Redis, Apache Beam).
Work on the implementation of new features, leaving things properly implemented, well documented and delivered on time.
Always be on the lookout for opportunities to create and improve. From our development process, up to final user’s experiences. 

JOB REQUIREMENT

Solid skills with Java. Python is a plus.
Solid understanding of databases.
Relevant experience with Google Cloud or AWS.
Superior analytical, conceptual and problem-solving skills.
The ability to learn and iterate quickly.
An obsession with agile and lean principles (GitHub, Trello).
Experience with complex text parsing and web scraping a plus.
Experience with data pipelines or ETL a plus.
Education and Experience 
University degree in computer science or similar education.
Minimum 2 years of experience with focus on backend development.
Strong verbal and written communication skills in English. 

WHAT'S ON OFFER

Internal training by ex-Silicon Valley CTO and award winning AI researcher
Singapore visit 2 times per year
Stipend for technical certification and training 
Career path coaching
Flexible hours
Health insurance
15 days vacation
13 month bonus

CONTACT

PEGASI – IT Recruitment Consultancy | Email: recruit@pegasi.com.vn | Tel: +84 28 3622 8666
We are PEGASI – IT Recruitment Consultancy in Vietnam. If you are looking for new opportunity for your career path, kindly visit our website www.pegasi.com.vn for your reference. Thank you!

Job Summary

Company Type:

product, AI platform

Technical Skills:

Big Data, Data, Python

Location:

Ho Chi Minh - Viet Nam

Working Policy:

Job ID:

J00287

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 details

Software 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 details

AI 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.

Negotiation

View details