Agentic Workflow Engineer

Zoho; India; Remote; INR 1,800,000 - 2,600,000; Full time

Job summary

Zoho is seeking an experienced Agentic Workflow / Multi-Agent Systems Engineer to design, develop, and stabilize autonomous multi-agent networks in a remote offshore setting. You will build production-grade, stateful agent systems where specialized AI entities collaborate and execute complex business operations independently.

The role demands 4–8 years in backend/distributed systems with 2+ years in autonomous multi-agent environments.

Qualifications

• 4–8 years in enterprise backend or distributed systems.
• 2+ years building production multi-agent environments.
• Strong Python or TypeScript with async programming.
• Certified ML Engineer or cloud ML credential required.

Responsibilities

• Design and develop multi-agent orchestration architectures.
• Build stateful deterministic and non-deterministic state machines.
• Implement secure, robust tool execution frameworks for enterprise APIs.
• Configure memory layers and cross-agent context sharing protocols.
• Establish HITL validation checkpoints for sensitive operations.
• Improve agent reasoning, planning, and self-correction loops.
• Implement observability and tracing to diagnose loops and monitor resources.

Skills

Python

TypeScript

Asyncio

API design

Relational databases

Distributed systems

Education

ML certification (AWS/GCP/Azure)

Tools

LangGraph

CrewAI

AutoGen

Semantic Kernel

LangSmith

Datadog LLM Observability

Kubernetes

Job description

Agentic Workflow / Multi-Agent Systems Engineer

• Employment Type: Contract
  
• Work Mode: Remote
  
• Location: Offshore
  
• Total Experience Required: 4 to 8 years
  
• Relevant Experience Required: 2+ years of dedicated experience developing multi-agent systems, autonomous AI agents, and complex task-planning state machines
  

We are seeking an experienced Agentic Workflow / Multi-Agent Systems Engineer to design, develop, and stabilize autonomous multi-agent networks within our enterprise environment. The ideal candidate will move past single-prompt solutions to build production-grade, stateful multi-agent systems where distinct specialized AI entities collaborate, share context, call enterprise APIs, handle execution failures gracefully, and execute multi-step business operations independently.

Key Responsibilities

• Design and develop multi-agent orchestration architectures using frameworks like LangGraph, CrewAI, AutoGen, or Semantic Kernel.
  
• Build stateful deterministic and non-deterministic state machines, managing conversation loops, task delegation logic, branching paths, and agent-to-agent communication networks.
  
• Implement secure, robust tool execution frameworks, empowering agents to dynamically call enterprise REST APIs, query databases via SQL, and process files within sandboxed runtime execution environments.
  
• Configure sophisticated multi-agent memory layers, setting up short-term transactional memory, long-term semantic vector storage, and cross-agent context sharing protocols.
  
• Establish structured human-in-the-loop (HITL) validation checkpoints, configuring human approval gates for sensitive agentic operations like financial triggers or external data mutations.
  
• Optimize agent reasoning and planning structures, implementing advanced cognitive patterns such as Reason and Act (ReAct), Plan-and-Solve, and self-reflection error-correction loops.
  
• Implement agentic observability and tracing frameworks using platforms like LangSmith , Arize Phoenix , or Datadog LLM Observability to diagnose stuck loops, trace call sequences, and monitor token consumption.
  

Requirements

• 4 to 8 years of core enterprise backend web engineering, asynchronous programming, or distributed systems experience, with 2+ dedicated years actively writing production-level application code for autonomous multi-agent environments.
  
• Strong technical mastery of Python or TypeScript, asynchronous programming (Asyncio), state-management patterns, API integration design, and relational databases.
  
• Deep structural understanding of LLM cognitive limits, tool calling hallucination profiles, infinite loop mitigation strategies, token budget planning, and multi-agent consensus protocols.
  
• Mandatory certification: Professional ML Engineer or Specialty Machine Learning credential from a major cloud vendor (AWS/GCP/Azure).
  

Preferred Qualifications

• Prior experience implementing multi-agent architectures handling multi-modal inputs (e.g., agents processing text, vision, and code generation simultaneously).
  
• Familiarity with deploying containerized multi-agent apps within Kubernetes microservice grids under tight networking isolation guidelines.