Senior Staff Engineer, Python & LLM at Nagarro1

REQUIREMENTS: • Total experience 7.5+ years • Strong hands-on expertise in LLM engineering and Python backend development. • Expertise in LLM Application Frameworks, Prompt Engineering with LLMs, Python, FastAP • Proven experience building and deploying applications using cutting‑edge LLMs (GPT‑4/5, Claude, Gemini, Mistral, LLaMA, Mixtral, DeepSeek, etc.). • Strong experience with RAG pipelines, embeddings, prompt engineering, and multi‑agent systems. • Hands-on expertise with LLM frameworks such as LangChain, LlamaIndex, Haystack, DSPy, AutoGen, CrewAI. • Deep knowledge of model fine‑tuning techniques such as LoRA, QLoRA, PEFT, adapters. • Experience deploying open‑source LLMs using vLLM, TGI, Ollama, LM Studio, Triton, etc. • Strong backend engineering experience with FastAPI (expert), Django or Flask, microservices, and distributed systems. • Experience implementing REST, GraphQL, and streaming APIs. • Hands-on experience with vector databases such as Pinecone, Weaviate, Milvus, Qdrant, FAISS, Chroma. • Knowledge of semantic search, hybrid search, embedding pipelines, and enterprise knowledge systems. • Strong understanding of cloud platforms (AWS, GCP, Azure), containers, and Kubernetes. • Experience with MLOps/LLMOps practices—CI/CD for ML workflows, monitoring, logging, tracing, and model lifecycle management. • Bachelor’s/Master’s in CS, AI, Data Science, or equivalent experience. • Excellent communication, collaboration, and problem-solving skills.RESPONSIBILITIES: • Design, implement, and optimize LLM-powered applications using leading and open‑source models. • Develop advanced prompt engineering, system prompts, and structured output pipelines. • Build RAG pipelines with hybrid search, embeddings, and custom retrieval strategies. • Develop multi-agent systems and autonomous AI workflows. • Fine‑tune, adapt, and serve foundation models using LoRA/QLoRA and modern inference engines. • Deploy and scale LLM workloads using vLLM, TGI, Ollama, or GPU/TPU-based systems. • Integrate multimodal models across text, image, audio, and video. • Build evaluation pipelines for hallucination detection, factual accuracy, quality scoring, and alignment. • Implement guardrails, moderation, and safety policies for AI systems. • Build scalable backend systems using FastAPI, microservices, event-driven architectures, and secure API frameworks. • Optimize backend performance, observability, and reliability. • Build ingestion pipelines for document processing, chunking, preprocessing, and semantic indexing. • Implement semantic, vector, and hybrid search at scale. • Deploy AI systems on cloud platforms, manage Kubernetes inference clusters, and optimize GPU utilization. • Set up CI/CD, automated testing, model versioning, and production monitoring for AI workflows. • Develop enterprise-grade search, knowledge systems, and document intelligence platforms. • Ensure robustness, security, and scalability in all AI and backend systems. • Stay updated with the latest GenAI, LLMOps, and backend engineering innovations and share knowledge within the technical community.