Python Engineer – Agentic AI & MCP Orchestration

• 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

• 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

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