Job highlights

- Bachelor s or Master s degree in Computer Science,Engineering,or a related field
- 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. .

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.

Valuable-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 .

📌 Senior AI Engineer LLM & Generative AI (Hubballi)
🏢 UFS Networks
📍 Hubballi