AI Solutions Architect (Software Engineering Transformation Lead)

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