AI Application Engineer

Key Responsibilities

• AI Product R&D & System Integration: Lead the core AI development for our B2C products and internal productivity platforms (e.g., AI Portal), including RAG-powered news chatbots and smart editorial assistants. Deeply integrate AI capabilities with content production, recommendation, and search systems to comprehensively boost the efficiency of news gathering, editing, and distribution.
• AI Product Insights & Boundary Exploration: Spearhead the discovery and definition of core AI business requirements. Leveraging a profound understanding of LLM capabilities and limitations, translate ambiguous business needs into clear technical roadmaps, driving the end-to-end delivery from requirement to architecture. Ensure AI solutions align with media professionalism and rigor while achieving deep synergy with business goals.
• Multimodal Technology Implementation: Drive the end-to-end implementation of multimodal AI technologies across the news production lifecycle. This includes, but is not limited to: automated generation of news images/charts, interview transcription and summarization, long-to-short video generation, and AI avatars/voice synthesis for news broadcasting.
• Model Fine-tuning & RAG Optimization: Fine-tune models to match our specific editorial style and brand voice. Tackle the digitization of historical news archives and cross-modal RAG retrieval. Build Prompt evaluation frameworks and Agent workflow benchmarking processes to continuously optimize context retrieval strategies and latency.
• Agile Adoption of Cutting-Edge AI: Maintain an extreme sensitivity to the evolution of Agents, RAG, and multimodal models. Lead the engineering validation and selection of frontier technologies. Establish a feedback loop for technical evaluation, driving production-grade deployment of new tech in complex scenarios through systematic experimental design and performance analysis.

Requirements

• At least a Bachelor's degree in Computer Science, Artificial Intelligence, or related technical fields. Candidates who are familiar with newsroom editorial workflows and proven experience applying AI in news scenarios (e.g., content production, smart assistance, or news lead monitoring) preferred.
• Open to both experienced professionals and fresh graduates:

• 3+ years of hands-on experience in AI implementation as a Project Owner. Capable of independently handling complex system architecture design and production deployment. (We embrace AI-assisted programming there are no rigid constraints on manual coding in specific languages).

• Fresh graduates with strong academic background or engineering potential demonstrated through involvement in LLM-related lab projects publishing papers at AI conferences as a core author or completing AI R&D internship at established technology companies.

• AI-Native Development (Vibe Coding): Fully embrace the Vibe Coding philosophy, being highly proficient with AI coding tools (e.g., Codex, Claude Code) for daily development. Possess solid computer science fundamentals and a software engineering mindset. We are language-agnostic (Python or Java are both fine) the focus is heavily on complex system architecture design with AI assistance, rigorous Code Review capabilities, and deep-dive bug troubleshooting skills.
• LLMs & Agents: Deep understanding of LLM architectural principles. Proficient in mainstream RAG and Agent development/evaluation frameworks. Extensive experience in Prompt Engineering or Context Engineering, and familiar with mainstream vector databases (e.g., Milvus, pgvector, Pinecone). Proficient in end-to-end SFT/DPO fine-tuning and preference alignment, with hands-on experience in constructing high-quality synthetic data and deep model adaptation preferred.
• Multimodal Skills: Profound understanding of the evolutionary path and technical boundaries of native multimodal LLMs. Experience in preprocessing, aligning, and fusing multimodal data. Production-level, full-stack development experience in image/audio/video generation, with the ability to lead solution design and low-level engineering implementation for complex multimodal tasks.
• Communication & Soft Skills: Excellent cross-departmental communication skills with high empathy. Able to collaborate smoothly with Product Managers and editorial teams, bridging product requirements and technical execution.
• Open Source & Academic Contributions: Active contributor to open-source AI projects, or published papers at AI conferences would be advantageous.