K
Senior Applied AI Engineer
KOS International Talent Group
5 hours ago
Full-time
On-site
Palo Alto, California, United States
KOS International is partnering with a leading global financial technology platform to hire a
Senior Applied AI Engineer
for its U.S. AI team. You will lead two primary streams: AI Coding tooling and developer experience
— bringing the engineering depth of Silicon Valley front-line teams (Claude Code, Cursor, Devin, Replit Agent, etc.) into our client. AI application paradigm exploration and dogfooding
— building small, frontier prototypes in internal scenarios that validate streaming UX, transparent agent steps, long-context memory, and other emerging patterns, and propagating them across the team through reference code, blog posts, and workshops. This role is about
craft and taste . You are not a platform architect, nor a BU project delivery engineer. You are the person who turns front-line Silicon Valley product engineering experience into multiplicative leverage for the entire AI Team. Key Responsibilities Introduce AI Coding tooling best practices
— Bring the engineering depth of Claude Code, Cursor, Devin, Replit Agent, and similar tools (Skills, hooks, subagents, long-context engineering, MCP integration) into our client. Produce engineer onboarding playbooks and advanced-usage standards. Set the bar for the Skill Marketplace
— Define Skill design conventions; personally deliver 5–10 reference-quality Skills as exemplars; raise team-wide Skill engineering quality through PR review. Prototype an AI-native engineering workflow
— Reimagine the spec → design → implementation → review → testing → release → operations lifecycle around AI tools, producing a reference workflow APAC teams can adopt. Build internal AI application prototypes (employee-facing scenarios)
— In OpenClaw, A2UI, role-bound AI assistants, and similar internal contexts, build small frontier prototypes that validate streaming UX, transparent agent steps, long-context memory, multi-agent collaboration, and other emerging patterns. Provide reference architecture code
— When APAC teams face hard architectural choices in new AI scenarios, deliver runnable "this is how Silicon Valley does it" reference implementations — not documents, but working code. Cross-team craft propagation
— One internal tech talk per month, one in-depth blog per quarter, and one onsite workshop in APAC per half year. Distill Silicon Valley AI application best practices into durable team assets. Architecture review participation
— As the AI Team's anchor in the U.S., participate in our client's AI architecture review process and provide frontier perspective and taste-based feedback at the application layer. Major Requirements Education : Top-tier CS / EE BS or above; MS preferred. 6+ years of software engineering experience , including at least 2 years focused on LLM applications. Senior AI application engineering experience : Has shipped AI applications from 0 → 1 in production (not demos). Must have at least one tour of duty as a core engineer at a top-tier Silicon Valley AI company or a leading AI product team — examples include Anthropic, OpenAI, Meta, and similar. Deep AI Coding tooling fluency : Claude Code, Cursor, Devin, or Replit Agent (at least one) is a daily-driver in your workflow, and you can articulate its engineering implementation. Strong understanding of the engineering tradeoffs in Skills, hooks, subagents, MCP, and long-context engineering. Agent application engineering depth : Solid grasp of ReAct, Plan-Execute, Reflection, Multi-Agent, and Orchestrator-Worker tradeoffs. Hands-on experience with LangGraph, AutoGen, CrewAI, OpenAI Swarm, or in-house frameworks. Strong full-stack engineering : Strong backend (Go / Python / TypeScript). Mid-level or above frontend (React / Next.js). Comfortable with end-to-end streaming rendering and interaction. Strong product sense and craft : Comfortable collaborating with PMs and designers; you have considered opinions and taste on AI application UX patterns (streaming, undo, context surfacing, agent-step progress, etc.). Public technical influence : A track record of public output — blog, talks, OSS contributions — sufficient to represent the team's craft externally. Self-direction and async collaboration : Capable of independently judging value priorities and driving prototype-to-dogfooding loops; effective at collaborating with APAC counterparts via documents, PRs, and asynchronous communication.
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Senior Applied AI Engineer
for its U.S. AI team. You will lead two primary streams: AI Coding tooling and developer experience
— bringing the engineering depth of Silicon Valley front-line teams (Claude Code, Cursor, Devin, Replit Agent, etc.) into our client. AI application paradigm exploration and dogfooding
— building small, frontier prototypes in internal scenarios that validate streaming UX, transparent agent steps, long-context memory, and other emerging patterns, and propagating them across the team through reference code, blog posts, and workshops. This role is about
craft and taste . You are not a platform architect, nor a BU project delivery engineer. You are the person who turns front-line Silicon Valley product engineering experience into multiplicative leverage for the entire AI Team. Key Responsibilities Introduce AI Coding tooling best practices
— Bring the engineering depth of Claude Code, Cursor, Devin, Replit Agent, and similar tools (Skills, hooks, subagents, long-context engineering, MCP integration) into our client. Produce engineer onboarding playbooks and advanced-usage standards. Set the bar for the Skill Marketplace
— Define Skill design conventions; personally deliver 5–10 reference-quality Skills as exemplars; raise team-wide Skill engineering quality through PR review. Prototype an AI-native engineering workflow
— Reimagine the spec → design → implementation → review → testing → release → operations lifecycle around AI tools, producing a reference workflow APAC teams can adopt. Build internal AI application prototypes (employee-facing scenarios)
— In OpenClaw, A2UI, role-bound AI assistants, and similar internal contexts, build small frontier prototypes that validate streaming UX, transparent agent steps, long-context memory, multi-agent collaboration, and other emerging patterns. Provide reference architecture code
— When APAC teams face hard architectural choices in new AI scenarios, deliver runnable "this is how Silicon Valley does it" reference implementations — not documents, but working code. Cross-team craft propagation
— One internal tech talk per month, one in-depth blog per quarter, and one onsite workshop in APAC per half year. Distill Silicon Valley AI application best practices into durable team assets. Architecture review participation
— As the AI Team's anchor in the U.S., participate in our client's AI architecture review process and provide frontier perspective and taste-based feedback at the application layer. Major Requirements Education : Top-tier CS / EE BS or above; MS preferred. 6+ years of software engineering experience , including at least 2 years focused on LLM applications. Senior AI application engineering experience : Has shipped AI applications from 0 → 1 in production (not demos). Must have at least one tour of duty as a core engineer at a top-tier Silicon Valley AI company or a leading AI product team — examples include Anthropic, OpenAI, Meta, and similar. Deep AI Coding tooling fluency : Claude Code, Cursor, Devin, or Replit Agent (at least one) is a daily-driver in your workflow, and you can articulate its engineering implementation. Strong understanding of the engineering tradeoffs in Skills, hooks, subagents, MCP, and long-context engineering. Agent application engineering depth : Solid grasp of ReAct, Plan-Execute, Reflection, Multi-Agent, and Orchestrator-Worker tradeoffs. Hands-on experience with LangGraph, AutoGen, CrewAI, OpenAI Swarm, or in-house frameworks. Strong full-stack engineering : Strong backend (Go / Python / TypeScript). Mid-level or above frontend (React / Next.js). Comfortable with end-to-end streaming rendering and interaction. Strong product sense and craft : Comfortable collaborating with PMs and designers; you have considered opinions and taste on AI application UX patterns (streaming, undo, context surfacing, agent-step progress, etc.). Public technical influence : A track record of public output — blog, talks, OSS contributions — sufficient to represent the team's craft externally. Self-direction and async collaboration : Capable of independently judging value priorities and driving prototype-to-dogfooding loops; effective at collaborating with APAC counterparts via documents, PRs, and asynchronous communication.
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