Artificial Intelligence Engineer

AI Service Commercialization & Backend Engineering

• Design, build, and optimize cost-efficient, scalable backend architectures for domain-specific AI services utilizing Computer Vision, vision-language models (VLMs), and Large Language Models (LLMs).
• Rapidly prototype new product features using AI-powered developer tools, then systematically harden them into highly stable, secure, and production-ready enterprise APIs.
• Deploy, monitor, and operate live AI inference services across distributed cloud and on-premise multi-GPU environments.

AI Agent Orchestration & Protocol Architecture

• Design and implement the runtime execution and orchestration layer for autonomous AI Agents, incorporating complex tool-calling, advanced reasoning patterns, and stateful memory management.
• Migrate existing services to Model Context Protocol servers, defining well-structured interfaces so autonomous Agents can seamlessly interact with backend infrastructure.
• Integrate multi-modal LLM APIs and establish systematic prompt-engineering structures to safely handle edge cases and runtime exceptions.

Reliability, SLAs & Cross-Functional Alignment

• Establish, monitor, and optimize Agent service SLAs, explicitly defining SLIs/SLOs around response latency, availability, and error rates.
• Architect resilient fallback mechanisms, retry policies, and circuit breaker strategies to guarantee high-availability service performance.
• Collaborate closely with global product management, business development, and core AI research teams to translate real-world client needs into high-performance technical systems.

Required

• Experience: 3+ years of professional experience developing, deploying, and operating live, production-grade AI-based services or cloud applications.
• CS Fundamentals: Exceptional command of core Computer Science fundamentals, including Operating Systems, computer system architecture, data structures, and algorithms.
• Technical Stack: High proficiency in at least one major programming language (Python, Java, or C/C++) and experience with modern backend frameworks (e.g., FastAPI, Flask, Django, or Spring Boot).
• Database Design: Proven experience building and optimizing relational databases using RDBMS ecosystems like PostgreSQL or MySQL.
• AI Tooling & Mindset: Previous exposure to designing LLM-backed services or AI Agents, paired with a history of using AI developer tools to supercharge personal development productivity.
• Global Readiness: Strong communication skills to proactively align technical solutions with business needs. No restrictions on international or overseas travel.

Preferred

• Advanced Agent Frameworks: Hands-on experience implementing multi-agent architectures or complex workflows using frameworks like LangChain, LangGraph, or custom-built loop logic through the execution of iterative reasoning loops.
• Network & API Optimizations: Deep understanding of designing real-time streaming responses, websockets, and highly asynchronous API architectures.
• Advanced Retrieval & Inference: Experience building optimized data pipelines for function calling, RAG (Retrieval-Augmented Generation), and handling high-traffic global inference under constrained GPU resources.
• Protocol Expertise: Foundational knowledge of MCP (Model Context Protocol) concepts or hands-on experience building custom MCP servers.

A rapidly growing, world-class AI technology firm is aggressively expanding its U.S. presence. Leveraging industry-leading AI to solve major industry challenges, the firm builds robust, automated product ecosystems. We are hiring a talented AI Software Engineer (3+ years experience) to join the foundational U.S. team. This role is at the absolute frontier of artificial intelligence, focusing on building domain-specific AI applications, developing sophisticated autonomous Agent architectures, and scaling global inference systems.

Why Join Us

• Ground-Floor Market Expansion: Experience the high-impact, high-ownership environment of launching a global tech firm’s U.S. presence, backed by deeply established international corporate resources.
• Frontier AI Engineering: Move beyond basic API integrations. You will architect autonomous Agent loops, design complex tool-calling mechanics, and build with cutting-edge infrastructure like the Model Context Protocol (MCP).
• End-to-End System Ownership: Take absolute ownership over the entire lifecycle-from rapid prototyping and proof-of-concept (PoC) validation to hardening services into production-ready, global-scale systems.
• Advanced Architecture Challenges: Tackle sophisticated optimization problems, including low-latency multi-modal streaming, multi-GPU resource scaling, and enterprise-grade SLA management.