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 (for Lead role)
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 (for Lead role)
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
