FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and operating adaptive retrieval pipelines and agentic RAG systems, with a strong focus on model-capability-driven innovations and performance measurement. Proficient in building production retrieval pipelines and implementing advanced retrieval strategies across various domains.
Highest-signal resume keywords
LLM Systems ExperienceRAG Systems ImplementationProduction Retrieval Pipeline DevelopmentEmbedding Models ProficiencyAgent Harness Runtime Knowledge
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Adaptive RetrievalMulti-Hop DecompositionPrompt EngineeringContext EngineeringText CleaningMultimodal Data ParsingReranking ModelsChunking StrategyAgentic RAG PatternsBenchmark Dataset Construction
Soft Skills
Problem AnalysisOriginal Idea GenerationRapid PrototypingExperiment Iteration
Tools & Technologies
BGEOpenAIQdrantMilvusPineconeWeaviatePi AgentAgentScope 2.0
Industry Keywords
Agent SystemsRetrieval EfficiencyLatency MeasurementTask Success RateMulti-Agent Collaboration
About the role
Key responsibilities & impact- Design and operate next-generation adaptive, self-correcting, and multi-hop retrieval pipelines
- Architect Agentic RAG systems with dynamic retrieval control, query decomposition, iterative retrieve-reflect-refine loops, and multi-agent retrieval collaboration
- Collaborate with researchers and engineers to define and implement model-capability-driven innovations
- Work on context management, long-term memory, subagent and multi-agent architectures, self-evolving agents, and real-world task execution
- Propose harness-domain and RAG-domain benchmarks and evaluation methodologies
- Construct benchmark datasets and define annotation strategies
- Measure and improve agent intelligence across domains, including retrieval efficiency, latency, groundedness, and task success rate
- Leverage multi-channel user feedback and real-world task data as research signals
- Design experiments and datasets to continuously improve agent and retrieval performance in production scenarios
Requirements
What you’ll need- 2-8+ Year hands-on experience with LLM, RAG and AI agent systems in production
- Hands-on experience building production retrieval pipelines end-to-end
- Experience with embedding models such as BGE and OpenAI
- Experience with vector stores such as Qdrant, Milvus, Pinecone, and Weaviate
- Experience with hybrid search, reranking models, chunking strategy, text cleaning, and multimodal data parsing
- Experience implementing Agentic RAG patterns, including Self-RAG, Corrective RAG, adaptive retrieval, multi-hop decomposition, and retrieve-reflect-refine loops
- Hands-on experience with Agent Harness runtimes such as Pi Agent, AgentScope 2.0, or equivalent orchestration frameworks
- Knowledge of session recovery, sandbox isolation, middleware/hook systems, multi-tenant runtime, plan/execute loops, and retrieval-grounded tool calling
- Deep familiarity with LLM and agent mechanisms, including LLM APIs, KV Cache, Agent Loop, Tool Use, Reasoning, Planning, Skills, MCP, Memory, Subagent, and Multi-Agent
- Strong grasp of Prompt Engineering and Context Engineering
- Ability to analyze ambiguous problems from first principles, generate original ideas, and drive research from 0 to 1
- Ability to rapidly translate ideas into runnable prototypes with tight experiment iteration loops
- Power user of agent products, including coding agents and general-purpose agents
- Proficient in vibe coding and AI-assisted workflows across unfamiliar languages, frameworks, and domains
- Strong learning velocity in software development
Benefits
Comp & perks- Competitive salary and company benefits
- Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)
- Opportunities for career growth and continuous learning
- Collaborate with world-class talent in a user-centric global organization with a flat structure
- Autonomy in an innovative environment
- Equal opportunity employer
- Diverse workforce