Tech Lead (C#/.NET - JTL AI Service Desk)
ABOUT CLIENT
JOB DESCRIPTION
JOB REQUIREMENT
WHAT'S ON OFFER
CONTACT
Job Summary
Company Type:
Outsource
Technical Skills:
.NET, ReactJS, Azure
Location:
Ho Chi Minh - Viet Nam
Working Policy:
Onsite
Job ID:
J02159
Status:
Active
Related Job:
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 detailsHead of Engineer - Tech Fraud & Scams VN
Ho Chi Minh
Product
- Management
- Backend
- Data Engineering
Develop an integrated roadmap for the strategic execution of Customer Onboarding and Mastery, Financial Crime, and Fraud's strategic ambitions. Lead engineering teams across these domains to drive outcomes, necessitating domain knowledge in these areas. Collaborate with business teams and product owners to validate requirements and monitor post-delivery performance. Oversee the runtime of applications in production and provide active operational support. Lead efforts for cyber security updates and ensure software currency versions remain up to date. Manage investment delivery across CET to maintain alignment between domains and ensure effective spending while providing insights on prioritization of spend and its effectiveness.
Negotiation
View detailsHead 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.