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Home/Jobs/AI Research Engineer: Reasoning & Retrieval
AI Research Engineer: Reasoning & Retrieval
Auric AI
Mumbai / Bengalore
3-5 years
2 days ago
$24.1K–37.3K/yr
Full-time
Onsite
Skills Required
RAG
Information Retrieval
Machine Learning
NLP
Description
Auric AI is building a reasoning system over millions of messy, multilingual intelligence documents, fully hosted in India on infrastructure they control. They are hiring one research engineer to own the architecture for reasoning and retrieval.
Company: Auric AI
Role: AI Research Engineer: Reasoning & Retrieval
Location: Bengaluru/Mumbai (Onsite)
Experience
- No experience requirement
- Built something that survived real, messy data
- Can reason about language models and retrieval mechanically
- Can explain why naive RAG fails on multi-hop temporal questions
Qualification
Responsibilities
- Own the architecture for a retrieval and reasoning system
- Design retrieval that can bound its own recall
- Build reasoning across many hops and sources
- Surface contradictions rather than averaging them away
- Propagate confidence explicitly through multi-step inference
- Externalise, compress, and reconstruct work that exceeds the context window
- Derive an approach for a hard open problem with no standard playbook
- Measure the system honestly
Additional Responsibilities
- Work with decades of documents in a dozen languages with no schema
- Handle the same entity written five different ways
- Ensure the system is interrogable by a person accountable for a decision
- Build entirely on open-weight models without fine-tuning or external APIs
- Work within an air-gapped, self-hosted environment
Nice To Have
- Publication record
- Repository that does something nobody asked for
More Skills
reasoning systems, retrieval, language models, multi-hop reasoning, temporal questions, decomposition, synthesis across sources, uncertainty propagation, architecture design, measurement
Other
- No fine-tuning
- No external APIs
- Self-hosted open-weight models
- Air-gapped deployment
- The models are fixed and architecture is the only lever
- No standard playbook exists
- Not this role: prompt templates, API integration, backend or UI, fine-tuning on labelled datasets
- They are reading for one thing: whether you can reason your way to an architecture, build it, and measure it honestly
Prepare for this role
Recommended resources to build the skills for this position. Sponsored.
Deep Learning Specialization
Coursera
Five-course deep learning series covering CNNs, RNNs, transformers, and ML strategy.
LangChain Chat with Your Data
Coursera
Build RAG applications with LangChain — document loading, splitting, embeddings, and retrieval.
Machine Learning Specialization
Coursera
Andrew Ng's updated ML course — regression, classification, neural networks, and decision trees.
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