Principal AI Agent Development Engineer/Architect
- Compensation
- Not disclosed
- Location
- Abu Dhabi, UAE; Hong Kong SAR; Kuala Lumpur, Malaysia
- Work setup
- Not specified
- Seniority
- Principal
- Employment
- Full time
- Experience
- 3+ years
- Category
- Architecture
- Posted
Skills
Must have
- Education & Experience: Bachelor's degree or above in CS/SE or related field; 6+ years of engineering experience with 3+ years focused on AI/Agent systems; candidates with 8+ years and hands-on 0-to-1
- Engineering Foundation: Proficient in Go or Python; capable of production-grade system design and TRD documentation.
- LLM Application Engineering: Deep expertise in LLM APIs, Prompt Engineering, ReAct patterns, and context window management (token budgeting / history compression / sub-agent compression).
- Production-Grade Agent Experience: Full 0-to-1 launch with continuous iteration loop — clearly distinguished from Demo/POC work.
- Agent Engineering Practice: Familiar with at least one orchestration framework (LangGraph / Dify / Eino / Embabel / OpenAI Agents SDK, etc.); hands-on experience with Multi-Agent collaboration (Agent
- Adjacent Agent Capabilities: Deep expertise in at least one of: RAG engineering (vector retrieval, Hybrid Search, Reranking) or evaluation systems (LLM-as-Judge, Golden Set, regression testing, A/B).
- Engineering Mindset: Ability to quantify effectiveness and cost (token optimization thinking); understanding of PII desensitization and prompt injection protection; ability to identify compliance boun
Nice to have
- 0 to-1 experience building an Agent simulation / benchmarking platform (τ-Bench scale)
- Private LLM deployment: vLLM / Triton / DeepSeek / Qwen / GLM
- Domain expertise: Crypto AI (risk control / KYC / compliance), AIOps (alert RCA / diagnostic tooling), customer service AI, code AI
- AI security research: published work on prompt injection, jailbreak defense, Smart Contract AI security, etc.
- Open-source contributions to LangChain / LangGraph / Dify / MCP / Eino / Vercel AI SDK or similar
- Public technical influence: blog posts / conference talks / papers / OSS projects
Full description
About Us
Established in 2018, Bybit is one of the world’s leading cryptocurrency exchanges and digital financial platforms, serving over 80 million users across more than 200 countries and regions. Powered by world-class technology and a user-first mindset, Bybit delivers a seamless ecosystem across trading, payments, wealth management, custody, institutional services, and Web3 — connecting users to the future of digital finance.
Our core values define how we build. We listen, care and improve to create products and experiences that put users first. Backed by a global team of ambitious builders, problem-solvers, and innovators, we foster a high-performance and fast-moving environment where talent is empowered to drive real impact at the global scale. Supported by 24/7 multilingual customer service and a strong commitment to innovation, we are shaping the future of finance through technology, collaboration, and bold execution.
Today, Bybit is recognized as one of the most trusted and transparent platforms in the digital asset industry, continuing to expand its global presence while building the infrastructure for the next generation of financial services.
(Suitable candidates has to relocate to Hong Kong, KL Malaysia or Abu Dhabi)
Key Responsibilities
- Agent SDK & Runtime — Design and implement the orchestration engine, runtime, and core primitives; ensure the agentic loop is both steerable and verifiable while remaining conversational and adaptive.
- MCP Matrix Expansion — Wrap internal systems as MCP servers / sub-agents / agent skills; unify tool discovery, dynamic registration, RBAC, rate limiting, and version management.
- Agent Development Lifecycle (ADLC) — Build toolchains adapted to the non-deterministic behavior of agents, covering the full loop: Spec → Eval → Build → Test → A/B → Canary → Monitoring → Iteration.
- End-to-End Ownership of Effectiveness & Cost — Own accuracy, resolution rate, cost, and latency metrics; build agent personalization optimization loops through data feedback.
Requirements
- Education & Experience: Bachelor's degree or above in CS/SE or related field; 6+ years of engineering experience with 3+ years focused on AI/Agent systems; candidates with 8+ years and hands-on 0-to-1 Agent platform delivery are strongly preferred.
- Engineering Foundation: Proficient in Go or Python; capable of production-grade system design and TRD documentation.
- LLM Application Engineering: Deep expertise in LLM APIs, Prompt Engineering, ReAct patterns, and context window management (token budgeting / history compression / sub-agent compression).
- Production-Grade Agent Experience: Full 0-to-1 launch with continuous iteration loop — clearly distinguished from Demo/POC work.