Design, develop, and stabilize production-grade multi-agent AI systems. Build orchestration architectures, state machines, secure tool execution, memory layers, human approval workflows, reasoning patterns, and observability frameworks. Integrate enterprise APIs, databases, file-processing environments, and vector storage while mitigating hallucinations, infinite loops, and token-budget issues. The role requires strong Python or TypeScript, asynchronous programming, distributed systems, and dedicated production experience with autonomous agents.
This is a remote position.
Agentic Workflow / Multi-Agent Systems Engineer
Job Details
- 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
- Mandatory Certification: Developer certification from a major AI or Cloud platform (e.g., Google Cloud Certified Professional ML Engineer, AWS Certified Machine Learning - Specialty, or verifiable framework specialization credentials)
Job Summary
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.
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