Generative AI Engineer

|| Urgent Hiring || Generative AI Engineer – LLMs, RAG, Agents & AI Security || Hyderabad Location ||
Location- Hyderabad
Profile- Generative AI Engineer – LLMs, RAG, Agents & AI Security
Experience- 3+ Years
Ctc- 10 LPA (Depends on interview)
Working days- 5 days (9:30am- 5:30 pm)
About the Role
We're hiring a GenAI Engineer who can build the full production GenAI stack - not just someone who wires up commercial LLM APIs. You should be equally comfortable building RAG systems, chatbots, AI agents, MCP integrations, and securing them for enterprise use. You'll have access to a great pool of AI native talent within an AI native company, working on cutting edge tools and projects with strong exposure to the latest in AI driven analytics.
Must-Have Skills
3+ yrs in ML/NLP/GenAI engineering, with production deployment experience
RAG (production-grade): document ingestion, chunking, embeddings, vector retrieval, hybrid search, reranking, evaluation — using FAISS, pgvector, Pinecone, Milvus, Weaviate, or Elasticsearch/OpenSearch
Conversational AI/Chatbots: multi-turn context, memory, session management, tool/function calling, guardrails, fallback strategies
AI Agents & Agentic Systems: tool calling, planning/execution workflows, multi-step reasoning, multi-agent systems — using LangChain, LangGraph, LlamaIndex, or Semantic Kernel
MCP (Model Context Protocol): building/integrating MCP servers and clients, secure auth for MCP-based systems
LLMOps: prompt/model versioning, evaluation pipelines, CI/CD, observability, monitoring (quality, hallucination, latency, cost, token usage)
AI Security & Governance: prompt injection, jailbreaking, data leakage, insecure tool calling, retrieval/knowledge-base poisoning; guardrails and access controls; familiarity with OWASP LLM Top 10, NIST AI RMF
Strong Python, PyTorch/TensorFlow, Hugging Face ecosystem
REST APIs/microservices, Git, testing, CI/CD
Willingness to learn and adopt AI tools within an advanced, AI native work ecosystem, including comfort using AI assistedanalysis, copilots, and emerging AI driven workflows as part of daily work.
Good-to-Have
Strong NLP background: text classification, NER, semantic search, summarization, embeddings
LLM training/fine-tuning from scratch: tokenization, pre-training, distributed training, LoRA/QLoRA, RLHF/DPO
Knowledge Graphs / GraphRAG (Neo4j, Neptune)
Distributed training (DeepSpeed, FSDP, Accelerate)
Inference optimization: vLLM, TensorRT-LLM, quantization (INT8/INT4)
Docker, Kubernetes, Cloud (AWS/Azure/GCP)
Multimodal/VLM experience
Education
CS/AI/ML/Data Science/Math/Stats/Computational Linguistics degree or equivalent experience.

Work arrangement
No