Location:  On-site Employment Type:  Full-time  Experience:  1-3 years

About Intugle

Intugle is a GenAI-powered platform that helps enterprise organizations to transform their legacy business applications with AI embedded intelligence.

About the Role

We are looking for an AI Engineer with hands-on experience building and deploying production-grade AI agents. You will design, develop, and operate multi-agent systems using the LangChain ecosystem (LangChain, LangGraph, LangSmith), integrating LLMs, tools, memory, and external APIs to automate complex workflows.

This is a high-ownership role — you will work across the full stack from agent design through deployment and observability.

Key Responsibilities

• Design and implement single and multi-agent pipelines using LangChain and LangGraph, covering orchestration, state management, and tool use
• Build custom tools, retrievers, and memory modules to extend agent capabilities
• Integrate agents with external systems via REST APIs, MCP servers, and function-calling interfaces
• Implement RAG pipelines — from chunking strategy and embedding selection to vector store indexing and retrieval optimization
• Write and refine prompts; maintain prompt versioning and regression test suites

Required Skills

Core Agent Engineering

• LangChain (chains, agents, tools, memory, callbacks)
• LangGraph (stateful multi-agent graphs, conditional edges, human-in-the-loop)
• Prompt engineering and structured output design
• Tool/function calling with OpenAI, Anthropic, or open-source models

LLM & ML Foundations

• Working knowledge of transformer-based LLMs and their API surface (context windows, temperature, sampling)
• Embedding models and similarity search
• Familiarity with fine-tuning concepts (LoRA, PEFT) — even if not hands-on

RAG & Knowledge Retrieval

• Vector databases (Pinecone, Weaviate, pgvector, Chroma, Qdrant)
• Chunking strategies, hybrid search, re-ranking
• Semantic layer design or ontology mapping (a plus)

Software Engineering

• Python (strong) — async patterns, typing, packaging
• REST API design and consumption
• SQL — ability to write and debug complex queries
• Git, CI/CD pipelines, environment management

Infrastructure & Deployment

• Docker and container orchestration basics
• Cloud deployment: AWS Lambda / ECS / EKS, or GCP Cloud Run, or Azure Container Apps
• Environment and secrets management
• Basic understanding of message queues (Kafka, SQS, Redis) for async agent pipelines

Observability & Evaluation

• LangSmith or equivalent tracing tools
• Evaluation frameworks for LLM outputs (LLM-as-judge, RAGAS, custom evals)
• Logging, alerting, and SLA monitoring for AI systems

Nice to Have

• Experience with Model Context Protocol (MCP) — building or consuming MCP servers
• Knowledge of Agent-to-Agent (A2A) communication patterns
• Graph databases (Neo4j) for semantic/schema retrieval
• AST-based code analysis or data lineage tooling
• Experience deploying on enterprise platforms (Azure ML, AWS SageMaker, Vertex AI)
• Contributions to open-source AI/ML projects