Agentic Workflow Engineer Job Columbus area,Ohio USA,Software Development

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 Lang Graph, CrewAI, Auto Gen, 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 Lang Smith , 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 Type Script, 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.