Responsibilities
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
Qualifications
Core Engineering Foundation
- 5+ 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
- Familiarity with AI security risks: prompt injection, adversarial inputs, data leakage in agentic contexts
Contact phone/Zalo: 0389 910 169 (Ngan)
