Interesting Job Opportunity: Senior Agentic AI Architect

MoreYeahs; Kolkata Metropolitan Area; On-site; INR 4,000,000 - 6,500,000; Full time

Job summary

MoreYeahs seeks a Senior Agentic AI Architect who has designed and deployed AI agent systems in production. You will build autonomous or semi-autonomous agents that reason, plan, use tools, and interact safely with external systems.

You will design reasoning loops, memory systems, tool integrations and escalation paths, implement guardrails, monitor deployments, and optimize cost and reliability of LLM-based workflows.

Qualifications

• 7+ years software engineering experience.
• 2+ years working with LLM or agent-based systems in production.
• Strong Python backend development skills.
• Experience with LangGraph, LangChain, CrewAI, AutoGen or similar.
• Experience building RAG pipelines and vector databases.
• Experience integrating AI systems with APIs and external services.

Responsibilities

• Design reasoning loops for planning, tool selection, and self-correction.
• Build multi-agent orchestration systems.
• Implement memory systems including short-term context and vector memory.
• Define escalation paths to human operators when needed.
• Implement guardrails to ensure safe and predictable agent behaviour.
• Deploy agents in production with monitoring and logging.

Skills

7+ years SWE

LLM/agent prod

Python backend

LangGraph

LangChain

CrewAI

AutoGen

RAG pipelines

Vector databases

API integration

Education

Bachelor's or Master's in CS/AI/SE

Tools

LangGraph

LangChain

CrewAI

AutoGen

Vector databases

APIs

Job description

Role Overview

We are seeking a Senior Agentic AI Architect who has already designed and deployed AI agent systems in production environments.

Role Overview

We are seeking a Senior Agentic AI Architect who has already designed and deployed AI agent systems in production environments. This role focuses on building autonomous or semi-autonomous AI agents that can reason, plan, use tools, and interact safely with external systems. The successful candidate will design agent reasoning loops, memory systems, tool integrations, and escalation paths while ensuring these systems operate reliably in real operational environments. This role requires someone who understands not only how to build agents, but also the practical pitfalls of operating them in production.

Core Responsibilities

Agent Architecture :

• Designing reasoning loops for planning, tool selection, and self-correction
• Building multi-agent orchestration systems
• Implementing memory systems including short-term context and vector memory
• Defining escalation paths to human operators when needed
• Implementing guardrails to ensure safe and predictable agent behaviour

LLM And Agent Systems Engineering

• Building production RAG pipelines using vector databases
• Implementing structured outputs and function calling
• Designing context management strategies for complex tasks
• Integrating agents with external APIs and business systems
• Applying prompt and context engineering techniques to improve reliability

Production Deployment

• Deploying agents used by real users and operational teams
• Implementing monitoring and logging for agent behaviour
• Managing token usage and LLM cost efficiency
• Diagnosing hallucinations, tool failures, and workflow breakdowns
• Implementing fallback and escalation mechanisms when agents encounter uncertainty

Production Experience (Critical Requirement)

• Multi-step agent reasoning loops
• Safe tool use and external system integration
• Human-in-the-loop escalation workflows
• Monitoring and reliability of LLM systems
• Guardrails to mitigate hallucinations and unsafe outputs
• Managing cost and performance of LLM-based systems

Candidates who have only experimented with agent frameworks or built prototypes without deploying production systems will not be considered a strong fit.

Essential Qualifications

• 7+ years software engineering experience
• 2+ years working with LLM or agent-based systems in production
• Strong Python backend development skills
• Experience with frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or similar
• Experience building RAG pipelines and working with vector databases
• Experience integrating AI systems with APIs and external services

Education

• Bachelors or Masters degree in Computer Science, Artificial Intelligence, Software Engineering, or equivalent practical experience.

(ref:hirist.tech)