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Job Responsibilities
- Building AI search agents- including ReAct, planning, and multi-agent architectures via custom implementation or frameworks like LangGraph, Dify, or CrewAI
- Building end-to-end RAG pipelines from ingestion, chunking, embeddings, and hybrid vector search, ideally using Opensearch
- Operating and monitoring vector/hybrid indexes (e.g. OpenSearch) in production environments
- Implement grounding and citation to link generated answers back to their exact source passages
- Automate evaluation using synthetic QA, retrieval-hit-rate tracking, and model-critique loops to continuously measure accuracy and detect drift
- Orchestrating external tools or knowledge bases and monitoring latency and cost at production scale
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field
- 3+ years of experience in developing AI systems, with a focus on retrieval-augmented generation (RAG)
- Proven track record in building and optimizing end-to-end RAG pipelines
- Experience with AI search agent development using frameworks like ReAct, LangGraph, Dify, or CrewAI
- Hands-on experience with OpenSearch or similar vector search technologies
- Proficiency in Python and relevant machine learning frameworks (e.g., PyTorch, TensorFlow)
- Strong understanding of data ingestion, chunking, embeddings, and hybrid vector search techniques
- Experience with monitoring and managing production environments
- Knowledge of grounding and citation techniques in AI-generated content
- Familiarity with synthetic QA datasets and evaluation metrics
Key Skills
Ranked by relevance
ai
machine learning
react
artificial intelligence
pytorch
python
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- Posted
- Jan 08, 2026
- Type
- Full-time
- Level
- Not Applicable
- Location
- Global Village
- Company
- CoinMarketCap
Industries
Information Services
Categories
Engineering
Information Technology
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