Senior AI Engineer LLM & Generative AI

Senior AI Engineer - LLM & Generative AI We are seeking a Senior AI Engineer with practical experience in LLMs, LangChain or similar frameworks, and Retrieval- mented Generation (RAG) systems. In this role, you will help design and deploy intelligent, secure, and scalable AI solutions that enhance Zscaler s products and internal automation tools. The position emphasizes AI backend development and orchestration using Python , cloud deployment (AWS preferred, GCP optional) , and integration of LLM-based services with light front-end development for chat interfaces, copilots, and dashboards. Responsibilities AI Solution Development: Build and maintain production-grade AI systems using LLMs, LangChain, and RAG pipelines to solve enterprise-scale problems. Model Integration: Implement, fine-tune, and evaluate LLMs using frameworks such as LangChain, LlamaIndex, Hugging Face, or OpenAI API . Backend & API Engineering: Develop scalable Python microservices and APIs for inference and knowledge retrieval. Deploy and operate services on AWS (preferred) or GCP using EKS , ECS , or Cloud Run . Implement observability, itoring, and autoscaling for production workloads. Retrieval- mented Generation (RAG): Design and optimize retrieval workflows using vector databases like FAISS , Pinecone , or Milvus . Integrate both structured and unstructured data into LLM pipelines for grounded responses. Front-End Integration: Collaborate with UI teams to integrate AI experiences into dashboards or chat UIs using React , Next.js , or TypeScript . Ensure seamless communication between front-end components and AI APIs. Cloud & DevOps: Containerize and deploy using Docker and Kubernetes . Implement CI/CD pipelines with tools like Jenkins, Gi b Actions, or Terraform. Cross-Functional Collaboration: Work closely with product, data science, and platform teams to deliver robust, secure, and impactful AI services. Continuous In ation: Stay current with LLM, RAG, and multi-agent system advancements and drive their adoption across products. Qualifications Must-Have Solid working experience with Python for AI service development, API integration, and data processing. Practical hands-on experience with LangChain , RAG pipelines , or similar developer frameworks. Familiarity with LLM integration , prompt design , and embedding-based retrieval . Experience deploying applications on AWS (preferred) or GCP , particularly with EKS , ECS , or Cloud Run . Proficiency with Docker , Kubernetes , and cloud-native service orchestration. Experience building RESTful or GraphQL APIs for AI and data services. Understanding of cloud security , scalability , and performance optimization principles. Good-to-Have Experience developing conver ional UIs, copilots, or AI-enabled dashboards (e.g., Slack apps, chat widgets). Familiarity with React , Next.js , or TypeScript for front-end feature integration. Exposure to Hugging Face Transformers , LlamaIndex , or LangGraph ecosystems. Knowledge of vector databases and data pipeline management . Understanding of compliance, privacy, and responsible AI practices in enterprise environments. Education & Experience Bachelor s or Master s degree in Computer Science, Engineering, or a related field. Typically 4-8 years of experience in software or AI engineering, including 2+ years of hands-on experience with LLMs, LangChain, or RAG systems .