AI/MLOps & Data Engineering Lead

AI Agent Ops Engineer

Ho Chi Minh - Viet Nam

Product

#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

Senior Data Engineer

Ha Noi - Viet Nam

Outsource

• Data Engineering

Triển khai dự án dữ liệu: Tham gia triển khai các dự án Data Platform, Data Warehouse, Data Lakehouse, AI Platform… chủ yếu trong lĩnh vực Tài chính - Ngân hàng. Thiết kế & Vận hành: Thực hiện thiết kế, cài đặt cấu hình và vận hành toàn bộ phần mềm, dịch vụ trong nền tảng dữ liệu (database, data ingestion, data governance, orchestration, query engine…). Hỗ trợ khách hàng & đối tác: Phát triển, tối ưu data pipeline, báo cáo; hỗ trợ migrate dữ liệu từ hệ thống cũ sang nền tảng mới. Đầu mối kỹ thuật: Làm việc trực tiếp với hãng và đối tác trong các trao đổi kỹ thuật. Tài liệu triển khai: Lập và quản lý tài liệu liên quan đến quá trình triển khai. Trình bày & Giới thiệu giải pháp: Tham gia trình bày, giới thiệu các giải pháp dữ liệu với khách hàng. Nghiên cứu & Phát triển: Nghiên cứu chuyên sâu các công nghệ và giải pháp dữ liệu theo định hướng công ty. Các công việc khác: Thực hiện nhiệm vụ khác theo yêu cầu.

Negotiation

Head of AI Factory

Ha Noi - Viet Nam

Product, Bank

• AI

Develop and execute enterprise-wide data science and AI strategy that aligns with business priorities. Provide guidance to C-level executives on leveraging data for business growth, risk mitigation, and operational efficiency. Promote the use of AI best practices among subsidiary companies. Lead the development of Predictive AI, including data and feature engineering, and model lifecycle management. Spearhead Generative AI initiatives, such as prompt frameworks, knowledge integration, and safety protocols. Assess and implement model solutions based on business, cost, risk, and performance considerations. Manage MLOps & LLMOps pipelines to ensure scalable deployment and automation for predictive and generative models. Create reusable AI assets and platforms, such as feature stores, model registries, and inference APIs. Work with IT and Data Architecture teams to create scalable data platforms, pipelines, and AI/ML infrastructure for both ML & GenAI, supporting both batch and real-time flow. Drive experimentation and research to keep up with practical emerging AI technologies. Establish ethical AI practices and ensure compliance with data privacy, regulatory, and security requirements. Collaborate with business units to advise on the application of AI/GenAI for business. Work with business and product owners to define problem statements, estimate value, build ROI models, and measure post-deployment outcomes. Provide leadership and management to enable subordinates to achieve AI Factory goals. Plan and allocate human resources and work with HR on recruitment, training, career development, and performance management. Develop talent and organizational capability in AI/GenAI, providing coaching and leadership to team members. Serve as a role model in building corporate culture and ensure consistent implementation of corporate cultural values.

Negotiation