AI Engineer – LLM & Agentic Systems

Navaris Digital; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Navaris Digital is seeking an AI Engineer to design and build production‑ready LLM applications, including RAG pipelines and multi‑agent systems. You will own end‑to‑end development from document indexing to context management, ensuring scalable, accurate, and well‑documented AI solutions.

The role emphasizes strong system design, modular code, and practical implementation with Python, RESTful APIs, and tool integrations across AI workflows, while collaborating with stakeholders to refine

Qualifications

• Experience building LLM-powered apps and RAG systems.
• Strong embeddings, vector DBs, and semantic search knowledge.
• Hands-on with LangChain, LangGraph, or similar.
• Proficient in Python and building RESTful APIs.
• Experience integrating external tools and APIs in AI workflows.
• Strong system design, microservices, scalable architectures.
• Ability to manage session state and context in conversational systems.
• Strong debugging and problem-solving skills; good communication.

Responsibilities

• Design end-to-end RAG pipelines with document processing and indexing.
• Integrate LLMs to generate context-grounded responses.
• Develop multi-agent AI systems with structured handoffs.
• Implement tool integrations for FAQs, updates, and external workflows.
• Manage per-user session context across agent interactions.
• Write clean, modular, well-documented code.
• Test and optimize system performance and retrieval accuracy.
• Document architecture decisions and trade-offs.
• Collaborate with stakeholders to refine requirements.

Skills

LLM Apps

RAG systems

Embeddings

Vector DBs

Semantic search

LangChain

LangGraph

Python

REST APIs

Tool integration

System design

Microservices

State management

Debugging

Communication

Tools

FAISS

Chroma

Pinecone

Azure Cognitive Search

Docker

Kubernetes

AWS

Azure

GCP

Job description

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 environment
• 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