TensorFlow Jobs

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Job Position Company Posted Location Salary Tags

Coin Market Cap Ltd

Hong Kong, Hong Kong

$92k - $117k

Coin Market Cap Ltd

Hong Kong, Hong Kong

$71k - $103k

Caiz

Remote

$80k - $150k

invictuscapital

Cape Town, South Africa

$175k - $240k

Binance

Taipei, Taiwan

Nearfoundation

Remote

$87k - $112k

CAIZ

Remote

$80k - $150k

Albusleo Ventures Inc.

Miami, FL, United States

$63k - $75k

Zscaler

Remote

$122k - $175k

Wf

New York, NY, United States

$115k - $206k

Nethermind

Remote

$84k - $115k

Genies

Remote

$45k - $63k

Nansen

Remote

$105k - $108k

Coinbase

Remote

$152k - $179k

DFINITY

Switzerland

$105k - $108k

Coin Market Cap Ltd
$92k - $117k estimated
Hong Kong
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AI/RAG engineer

Global / Hong Kong / Kuala Lumpur / London / Penang / Singapore / Taipei
CMC /
Full-time /
Remote

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Job Responsibilities
1. Building AI search agents- including ReAct, planning, and multi-agent architectures via custom implementation or frameworks like LangGraph, Dify, or CrewAI.
2. Building end-to-end RAG pipelines from ingestion, chunking, embeddings, and hybrid vector search, ideally using Opensearch. 
3. Operating and monitoring vector/hybrid indexes (e.g. OpenSearch) in production environments.
4. Implement grounding and citation to link generated answers back to their exact source passages.
5. Automate evaluation using synthetic QA, retrieval-hit-rate tracking, and model-critique loops to continuously measure accuracy and detect drift.
6. Orchestrating external tools or knowledge bases and monitoring latency and cost at production scale.

Qualifications
1. Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
2. 3+ years of experience in developing AI systems, with a focus on retrieval-augmented generation (RAG).
3. Proven track record in building and optimizing end-to-end RAG pipelines.
4. Experience with AI search agent development using frameworks like ReAct, LangGraph, Dify, or CrewAI.
5. Hands-on experience with OpenSearch or similar vector search technologies.
6. Proficiency in Python and relevant machine learning frameworks (e.g., PyTorch, TensorFlow).
7. Strong understanding of data ingestion, chunking, embeddings, and hybrid vector search techniques.
8. Experience with monitoring and managing production environments.
9. Knowledge of grounding and citation techniques in AI-generated content.
10. Familiarity with synthetic QA datasets and evaluation metrics.
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