AI Agent Engineer

Key Responsibilities:

• Backend System Development:
  
  Architect, implement, and maintain scalable backend services and APIs using Python (FastAPI, Flask, Django) to support AI agent operations.
• AI Agent & MCP Integration:
  
  Develop, deploy, and orchestrate autonomous AI agents, and leverage MCP to coordinate multiple AI models and agent workflows, ensuring seamless interoperability and orchestration.
• Agent Orchestration & Workflow:
  
  Utilize frameworks like LangGraph and MCP to design, compose, and manage complex agent workflows, multi-agent coordination, and state management.
• System Integration:
  
  Integrate external data sources, third-party APIs, vector databases, and machine learning models to enhance agent capabilities.
• Performance & Reliability:
  
  Optimize system performance, ensure high availability, and implement robust logging, monitoring, and error-handling solutions.
• Collaboration:
  
  Work cross-functionally with front-end developers, data scientists, and product teams to translate requirements into technical designs and deliverables.
• Security & Compliance:
  
  Implement best practices for data security, privacy, and compliance in all backend operations.

Required Skills & Qualifications:

• Strong experience with backend Python frameworks (FastAPI, Flask, Django), RESTful API design, and database management (SQL/NoSQL).
• Proven expertise in integrating and orchestrating AI agents, LLMs, or multi-agent systems in production environments.
• Hands-on experience with MCP (Model Composition/Coordination Platform) for coordinating and managing AI models and agent workflows.
• Familiarity with agent workflow orchestration tools such as LangGraph.
• Experience with containerization (Docker), CI/CD pipelines, and cloud infrastructure (AWS, GCP, Azure).
• Knowledge of vector databases, data pipelines, and scalable distributed systems.
• Excellent problem-solving abilities, attention to detail, and communication skills.