Tóm tắt công việc
AI-Native Engineering Practice - Technical Ownership:
Own and continuously evolve KMS's AI-native SDLC operating model at KMS: agent workflow designs, verification gates, context management standards, and eval frameworks
Build and lead multi-agent systems using orchestration layers such as Claude Code, GitHub Copilot Workspace, Cursor, LangGraph, CrewAI, or equivalent - from prototype to production
In collaboration with the Director of Engineering, contribute to and help maintain KMS's AI toolchain selection criteria - evaluating tools with engineering rigor, not hype - and publishing internal guidance on when AI helps and when it hurts
Establish prompt engineering standards, agent evaluation (evals) loops, and AI output quality gates across the delivery organization
Capability & Standards Leadership
Prior experience in a lead, principal, or staff engineer role with demonstrated cross-team influence
Experience in outsourcing, consulting, or multi-client delivery environments
Track record of building or leading an internal community of practice, guild, or AI adoption program
Develop and continuously evolve KMS's AI-native SDLC playbook - standards, workflow templates, case studies, and guardrails that delivery teams can adopt immediately
Design and lead internal upskilling programs (workshops, pairing) that move engineers from AI-assisted to AI-native working patterns
Track the AI capability frontier - model improvements, new agent frameworks, emerging risks - and translate signals into timely updates to KMS's practices
Client Delivery
Work closely alongside KMS Delivery Teams - as an AI transformation advisor and execution partner - identifying the highest-value automation opportunities across the SDLC and coordinating with the team to bring them to life
Design and deploy agent-orchestrated workflows tailored to each client's stack, team maturity, and delivery context - with measurable ROI
Build business cases for AI-native adoption with clients and
account managers, framing the value in terms of velocity, quality, and cost
Represent KMS's AI-native engineering capabilities in client conversations, QBRs, and RFP responses - acting as a credible technical authority
Perks You'll Enjoy
Working in one of the Best Places to Work in Vietnam
Building large-scale & global software products
Working & growing with Passionate & Talented Team
Diverse career opportunities with Software Services, Software Product Development, IT Solutions & Consulting
Flexible working time
Various training on hot-trend technologies, best practices and soft skills
Company trip, big year-end party, team building, etc.
Fitness & sport activities: football, tennis, table tennis, badminton, yoga, swimming...
Joining community development activities: 1% Pledge, charity every quarter, blood donation, public seminars, career orientation talks,...
Free in-house entertainment facilities (foosball, ping pong, gym...), coffee, and snacks (instant noodles, cookies, candies...)
And much more, join us and let yourself explore other fantastic things!
Core Engineering Foundation
8+ years of professional
software engineering, with a proven track record of leading technical initiatives that span multiple teams or systems
Deep hands-on experience across the full SDLC: from requirements and architecture through testing, deployment, and production operations
Demonstrated ability to lead technical direction - setting standards, reviewing architecture decisions, and influencing without direct authority
Strong command of software architecture principles: system decomposition, API design, scalability, observability, and failure mode reasoning
Proficiency in at least one primary language: Python, TypeScript/JavaScript, Java, .Net or Go - with experience across multiple layers of the stack
AI & Agentic Systems Fluency
Proven, production-grade experience with AI coding agents as a core part of your daily workflow
Strong understanding of LLM API integration in production: context window management, latency and cost tradeoffs, model selection criteria, fallback strategies, and output reliability patterns
Experience or strong interest in multi-agent orchestration patterns: task decomposition, agent communication, tool use, memory, and eval loops
Working knowledge of RAG architectures, embedding strategies, and how to ground AI agents in domain-specific, proprietary knowledge bases
Ability to design and run AI evals: you can define quality metrics, build evaluation datasets, detect regressions, and use quantitative signals to improve agent behaviour over time
Nice to have
Experience with agentic frameworks: LangGraph, CrewAI, AutoGen, or similar orchestration patterns
MLOps knowledge: model deployment, monitoring, drift detection, A/B testing in production
Proficient daily use of AI coding tools (Copilot, Cursor, Claude Code) across the full SDLC
Able to decompose complex tasks, provide effective context, and apply chain-of-thought and multi-step prompting workflows
Experience with agentic workflows and AI-driven development frameworks
Hands-on experience integrating AI tools with other systems via MCP (Model Context Protocol) or equivalent
Awareness of emerging AI tools and willingness to evaluate and adopt new capabilities
