AI Agent Engineer

Agentic RAG & Engineering: Design and operate next-generation retrieval pipelines moving beyond static retrieve-once patterns to adaptive self-correcting and multi-hop retrieval workflows; architect Agentic RAG systems with dynamic retrieval control query decomposition iterative retrieve-reflect-refine loops and multi-agent retrieval collaboration

Frontier Harness: Collaborate deeply with researchers and engineers to define and implement model-capability-driven innovations including context management long-term memory subagent and multi-agent architectures self-evolving agents and real-word task execution

Benchmarking & Evaluation: Propose harness-domain and RAG-domain benchmarks and evaluation methodologies; construct benchmark datasets define annotation strategies and systematically measure and improve agent intelligence across domains including retrieval efficiency latency groundedness and task success rate

Real-world Feedback Loops: Leverage multi-channel user feedback and real-world task data as primary research signals; design experiments and datasets to continuously improve agent and retrieval performance in production scenarios

1 Year hands-on experience with LLM RAG and AI agent systems in production

RAG & Agentic RAG Engineering: Hands-on experience building production retrieval pipelines end-to-end embedding models (BGE OpenAI etc.) vector stores (Qdrant Milvus Pinecone Weaviate) hybrid search (keyword vector) reranking models; deep understanding of chunking strategy text cleaning and multimodal data parsing; experience implementing Agentic RAG patterns Self-RAG Corrective RAG adaptive retrieval multi-hop decomposition retrieve-reflect-refine loops

Agent Harness Engineering hands-on experience with Agent Harness runtimes (Pi Agent AgentScope 2.0 or equivalent orchestration frameworks): session recovery sandbox isolation middleware/hook systems multi-tenant runtime plan/execute loops and retrieval-grounded tool calling

LLM & Agent Fundamentals: Deep familiarity with LLM and agent mechanisms LLM APIs KV Cache Agent Loop Tool Use Reasoning Planning Skills MCP Memory Subagent Multi-Agent; strong grasp of Prompt Engineering Context Engineering

Independent Research Capability: Can analyze ambiguous problems from first principles generate original ideas and drive research from 0 to 1; able to rapidly translate ideas into runnable prototypes with tight experiment iteration loops

Heavy Agent User: Power user of agent products (coding agents general-purpose agents); agent tools are already integrated into your daily work and life; you have taste and judgment about model behavior

AI-native Engineering: Proficient in vibe coding ships fast using AI-assisted workflows across unfamiliar languages frameworks and domains; strong learning velocity in software development

Deep hands-on experience with agent products such asClaude Code OpenClaw Cowork Manus or equivalent already integrated into your workflow or daily life

RAG evaluation: Experience with RAGAS TruLens or custom benchmarking pipelines for retrieval quality groundedness and latency profiling

GraphRAG / knowledge graph-augmented retrievalexperience

Experience withPi Agent AgentScope 2.0orother Agent Harness: middleware composition multi-tenant session management plugin architecture sandbox backends

Background in model training RLHF or modelsystem co-design

LiteLLM / multi-provider proxy experience

Kubernetes/EKS: pod isolation resource management secrets handling

Security engineering: prompt injection defense sandbox hardening guardrail design

Deep hands-on experience with agent products such asClaude Code OpenClaw Cowork Manus or equivalent already integrated into your workflow or daily life

RAG evaluation: Experience with RAGAS TruLens or custom benchmarking pipelines for retrieval quality groundedness and latency profiling

GraphRAG / knowledge graph-augmented retrievalexperience

Experience withPi Agent AgentScope 2.0orother Agent Harness: middleware composition multi-tenant session management plugin architecture sandbox backends

Background in model training RLHF or modelsystem co-design

LiteLLM / multi-provider proxy experience

Kubernetes/EKS: pod isolation resource management secrets handling

Security engineering: prompt injection defense sandbox hardening guardrail design