About The Role We are seeking an AI Engineer to design and build production-ready LLM applications, including Retrieval-Augmented Generation (RAG) pipelines and multi-agent AI systems. The role focuses on strong system design, clean architecture, and practical implementation of intelligent, tool-integrated solutions. You will be responsible for end-to-end development — from document indexing and semantic retrieval to agent orchestration and context management — ensuring scalable, accurate, and well-documented AI systems.

Responsibilities Design and implement end-to-end RAG pipelines including document processing, vector indexing, and semantic retrieval

Integrate LLMs to generate accurate, context-grounded responses

Develop and orchestrate multi-agent AI systems with structured handoffs

Implement tool integrations for FAQs, seat updates, and external workflows

Manage per-user session context and state across agent interactions

Write clean, modular, and well-documented code

Test, debug, and optimize system performance and retrieval accuracy

Document architecture decisions, assumptions, and trade-offs

Collaborate with stakeholders to refine requirements and improve solutions Requirements Experience building LLM-powered applications and RAG systems

Strong understanding of embeddings, vector databases, and semantic search

Hands-on experience with frameworks such as LangChain, LangGraph, or similar

Proficiency in Python and building RESTful APIs





Experience integrating external tools and APIs within AI workflows

Solid understanding of system design, microservices, and scalable architectures

Ability to manage session state and context in conversational systems

Strong debugging, problem-solving, and optimization skills

Clear communication skills with the ability to explain technical decisions and trade-offs Nice to Have Experience with agent orchestration frameworks and multi-agent architectures

Hands-on experience with FAISS, Chroma, Pinecone, or Azure Cognitive Search

Experience deploying AI systems using Docker and Kubernetes

Familiarity with cloud platforms such as AWS, Azure, or GCP

Experience with evaluation frameworks for LLM performance and retrieval quality

Knowledge of prompt engineering and guardrail implementation techniques

Experience building production-grade chatbots or conversational AI systems

Understanding of CI/CD pipelines and DevOps best practices What We Offer Opportunity to work on cutting-edge Generative AI and agentic systems

High ownership and autonomy in technical decision-making

Exposure to real-world, production-grade AI implementations

Collaborative and innovation-driven work setting

Flexible work arrangements

Competitive compensation and growth opportunities

Opportunity to shape AI architecture and best practices from the ground up

Continuous learning and skill development in emerging AI technologies

📌 AI Engineer – LLM & Agentic Systems (India)
🏢 Navaris Digital
📍 India