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Applied AI Engineer (LLM/RAG/NLP)

Specurity is a stealth-mode startup founded by a team of world-ranked security researchers and seasoned operators. We're building quietly and deliberately, and we're now assembling a core founding team of exceptional engineers to build the core of our platform alongside us.

We're looking for people who thrive on autonomy, take real ownership, and want to do the most important work of their careers in a fast, high-trust environment. You'll work directly with the founders, ship continuously, and help define how the company operates from day one.

We can't say much yet about what we're building — but we can promise hard problems, serious talent around you, and a front-row seat to something we believe will matter.

What You’ll Do

• Build and optimize the RAG (Retrieval-Augmented Generation) pipeline that powers our platform’s context-aware AI features

• Design chunking strategies, embedding pipelines, and retrieval mechanisms for domain-specific knowledge bases

• Develop and refine prompts and prompt chains for complex multi-step AI workflows

• Build evaluation systems to measure AI output quality — accuracy, relevance, hallucination detection

• Optimize LLM inference for latency and cost across different model providers

• Implement context window management strategies for large knowledge base interactions

• Build semantic search and similarity matching features across structured and unstructured data

What We’re Looking For

• 4-5 years of experience with at least 2 years working with LLMs or NLP systems

• Hands-on experience with RAG architectures — vector databases, embedding models, retrieval optimization

• Strong prompt engineering skills with experience across multiple LLM providers (Claude, GPT, open-source)

• Experience with LangChain, LlamaIndex, or similar LLM orchestration frameworks

• Proficiency in Python with strong software engineering fundamentals

• Understanding of embedding models (text-embedding-ada, Sentence Transformers, Cohere)

Nice to Have

• Experience with fine-tuning LLMs or training custom models

• Background in information retrieval or search systems

• Familiarity with cybersecurity domain

• Familiarity with vector databases (Pinecone, Weaviate, Chroma, Qdrant)

• Experience with evaluation frameworks (RAGAS, DeepEval, or custom)

Annual Salary : ₹30-50 LPA ($33k-$53K)

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Are you able to work in our Gandhinagar (Gift City) office?