Senior Applied AI / LLM Engineer

Compensation: ₹8L – ₹12L • No equity

We are looking for someone who can design and build the intelligence layer powering our next-generation education platform. You will work across LLMs, RAG, AI agents, model routing, personalization, evaluation, AI infrastructure, and ML/data intelligence. This is a hands-on engineering role for someone who wants to build production-grade AI systems rather than simply integrate APIs. You will own important architectural decisions and help create AI systems that are scalable, cost-efficient, reliable, personalized, and flexible across multiple foundation-model providers. ### Requirements * Strong understanding of LLM application architecture and modern Generative AI systems * Hands-on experience with OpenAI, Anthropic, Google Gemini, or other foundation-model APIs * Experience building production applications using LLMs, structured outputs, tool/function calling, and prompt/system design * Strong understanding of RAG architectures, embeddings, vector databases, semantic search, hybrid search, chunking, reranking, and retrieval pipelines * Experience designing AI orchestration, model routing, context management, and multi-step AI workflows * Strong Python programming and software engineering fundamentals * Understanding of APIs, asynchronous processing, databases, caching, queues, and scalable backend systems * Ability to evaluate AI systems based on quality, accuracy, latency, reliability, token usage, scalability, and cost * Understanding of AI evaluation, hallucination detection, grounding, output validation, and reliability mechanisms * Ability to design systems that remain flexible as models, providers, requirements, and product usage evolve * Strong understanding of when to use LLMs and when deterministic code, databases, search, rules, or traditional ML would be more appropriate * Ability to make engineering trade-offs and choose the simplest reliable architecture for a given problem # Responsibilities * Design and own Novevex's AI architecture across LLM integration, AI orchestration, model routing, context management, RAG, vector search, knowledge systems, AI agents, personalization, evaluation, safety, reliability, and observability * Build production-grade AI applications on top of OpenAI, Anthropic, Google Gemini, and other suitable foundation models * Design vendor-flexible model architectures that allow Novevex to switch between models and providers without redesigning the entire platform * Evaluate and select models based on quality, capability, latency, reliability, token usage, infrastructure requirements, and cost * Design cost-effective AI architectures using techniques such as model routing, caching, retrieval, batching, precomputation, and appropriate model selection * Build scalable AI systems capable of supporting growth from hundreds of students to hundreds of thousands or millions of users * Design and implement RAG and knowledge-intelligence systems including document ingestion, chunking, embeddings, vector search, hybrid retrieval, metadata filtering, reranking, grounding, and context pipelines * Build personalized AI experiences using student profiles, class, board, subject, chapter, topic, performance, learning history, previous interactions, weaknesses, and current learning objectives * Develop intelligent AI workflows that combine student context, academic knowledge, retrieval, model selection, generation, validation, and personalization * Build AI evaluation pipelines to measure correctness, relevance, hallucination, grounding, consistency, explanation quality, question quality, difficulty, and structured-output validity * Implement reliability mechanisms including validation, guardrails, fallback models, error handling, monitoring, and quality checks * Design AI observability systems to monitor model performance, latency, token consumption, costs, failures, and user-level outcomes * Work with student-performance data and help design the interface between LLM-based intelligence and future ML/data-science systems * Collaborate with backend, product, and data teams to integrate AI capabilities into the broader Novevex platform * Continuously research and evaluate new AI models, frameworks, architectures, and techniques that can create meaningful product advantages * Make architecture decisions based on business requirements, expected scale, reliability, maintainability, performance, and cost rather than technology trends alone # Technical Skills ## Required ### LLM / Generative AI * OpenAI / Anthropic / Gemini APIs * LLM application architecture * Context engineering * Structured outputs * Tool/function calling * Prompt/system design * Model evaluation ### RAG / Retrieval * Embeddings * Vector databases * Semantic search * Hybrid search * Metadata filtering * Reranking * Document pipelines ### AI Engineering * Python * API development * Asynchronous workflows * Caching * Retries/fallbacks * Observability * Performance optimization ### Architecture * Scalable system design * API/service architecture * Cloud infrastructure concepts * Distributed systems fundamentals * Security and reliability * Cost optimization # Strongly Preferred * AI agents / agentic workflows * Multimodal AI * Recommendation systems * Personalization engines * Knowledge graphs / concept graphs * ML fundamentals * Model routing * Inference optimization * AI experimentation * Evaluation frameworks * Production AI systems # What We Are Not Looking For * A prompt engineer who only writes prompts * Someone who only connects an LLM API * An academic ML researcher who only wants to train models * Someone focused on building a foundation LLM * Someone who chooses technology based only on popularity * Someone who ignores API/infrastructure cost * Someone who builds a demo but cannot design a production system