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Python Engineer – Agentic AI & MCP Orchestration

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Location:
United States , Jersey City, NJ/ Pennington, NJ

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Contract Type:
Not provided

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Salary:

135000.00 USD / Year

Job Responsibility:

  • Design and develop Python-based agentic applications capable of orchestrating autonomous and semi-autonomous workflows
  • Build, configure, and maintain MCP servers to expose tools, data sources, or domain capabilities to LLM agents
  • Implement and configure MCP clients to interact with multiple MCP tools, AI models, and external systems
  • Develop orchestration logic to coordinate multi-agent behaviors, tool execution, validations, and decision routing
  • Integrate LLMs with internal systems using structured prompts, tool definitions, and safe execution patterns
  • Optimize agent workflows for reliability, performance, security, and cost
  • Create reusable frameworks for agent tools, context handling, memory, and reasoning cycles
  • Collaborate with architecture, platform, and product teams to align on engineering best practices
  • Implement observability: logging, tracing, and monitoring of agent reasoning steps and tool calls
  • Document MCP schemas, agent behaviors, tool interfaces, and operability guidelines

Requirements:

  • Strong hands-on experience in Python for building production-grade applications
  • Experience developing agentic AI applications (multi-agent workflows, tool-using agents, autonomous task execution)
  • Expertise with AI orchestration frameworks (agents, tools, planners, workflow controllers)
  • Practical experience setting up and configuring Model Context Protocol (MCP) servers
  • Ability to implement and integrate MCP Clients with external systems and AI models
  • Proficiency working with LLMs, prompt engineering patterns, and structured output handling
  • Experience with API integration, event-driven interactions, and tool/skill registration for agents
  • Strong understanding of asynchronous Python (asyncio, concurrency patterns)
  • Experience working with CI/CD, Git, testing frameworks (pytest), and secure coding practices
  • Ability to translate business problems into agent-driven automation workflows
  • Strong debugging and troubleshooting of distributed agent behavior and orchestration flows
  • Familiarity with governance, model safety constraints, and responsible AI usage patterns
  • Strong documentation habits for API schemas, MCP interface definitions, and agent lifecycle behavior
  • Effective communication with architects, product teams, and business stakeholders
  • Comfort working in Agile environments with rapid experimentation and iteration

Nice to have:

Familiarity with vector stores, embeddings, RAG pipelines, and memory architectures

Additional Information:

Job Posted:
March 21, 2026

Employment Type:
Fulltime
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