About Us

Automation Anywhere is the leader in Agentic Process Automation (APA), transforming how work gets done with AI-powered automation. Its APA system, built on the industry’s first Process Reasoning Engine (PRE) and specialized AI agents, combines process discovery, RPA, end-to-end orchestration, document processing, and analytics—all delivered with enterprise-grade security and governance. Guided by its vision to fuel the future of work, Automation Anywhere helps organizations worldwide boost productivity, accelerate growth, and unleash human potential.

Job Summary

We are seeking a Cloud Generative AI and RAG Engineer with 2–3 years of professional experience to build, scale, and optimize GenAI applications. He/She will focus on building robust Retrieval-Augmented Generation (RAG) pipelines, writing clean Python production code, and deploying these solutions onto secure cloud infrastructure.

Location : Bangalore(Onsite)
Experience - 5 - 6+ Years

Key Responsibilities

• GenAI & RAG Development: Design, implement, and optimize production-grade Retrieval-Augmented Generation (RAG) pipelines to improve LLM accuracy.
• Python Engineering: Write clean, modular, and highly efficient backend code using Python frameworks like FastAPI or LangChain.
• Cloud Architecture: Deploy, secure, and scale GenAI applications on cloud platforms (AWS, GCP, or Azure).
• Vector Database Management: Manage and tune Vector Databases to ensure fast, relevant semantic search retrieval.
• API Integration: Connect application frontends and internal data systems with enterprise LLMs via secure APIs.
• System Monitoring: Track LLM token usage, response latency, and retrieval accuracy in production.

Required Skills & Qualifications

• Experience: 5–6+ years of cloud engineering experience and AI solution development.
• Python Mastery: Expert-level Python skills, with a focus on asynchronous programming and API development.
• GenAI Ecosystems: Hands-on experience using frameworks like LangChain,  LlamaIndex, Hugging Face or any similar framework.
• Vector Databases: Practical experience with databases such as Vespa, Pinecone, Milvus, Chroma, or any similar database.
• Cloud Infrastructure: Proven experience in deploying band  managing cloud compute (Docker, Kubernetes) and serverless services.
• Prompt Engineering: Ability to systematically design and optimize prompts for specific business use cases.

Preferred Qualifications

• Familiarity with fine-tuning open-source LLMs (e.g., Llama, Mistral).
• Experience setting up MLOps/LLMOps tracking tools (e.g., LangSmith, Weights & Biases).
• Cloud certifications focusing on DevOps or Developer pathways.

Education

• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field