AI Research Engineer: Reasoning & Retrieval

Home/Jobs/AI Research Engineer: Reasoning & Retrieval AI Research Engineer: Reasoning & Retrieval Auric AI Mumbai / Bengalore 3-5 years Today $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
• No degree requirement 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. More RAG jobs Voice AI Intern Atlys Delhi Today AI Product Engineer Cognologix Technologies Pvt. Ltd. Pune Today Senior GenAI Engineer FindQ India Today GenAI Trainee Larsen & Toubro Powai Today Forward Deployed AI Engineer Skit.ai Bengaluru Today Software Engineer II, AI/ML DigitalOcean Bengalore Today