Python Backend Developer

Qualifications

• 4+ years of software development experience with production systems
• Strong proficiency in Python and/or Type Script/Node.js
• Deep experience with REST APIs, Graph
  
  QL, and various integration patterns
• Understanding of JSON-RPC, Web Socket, or similar RPC protocols
• Expertise in async/await patterns and concurrent programming
• Experience with authentication mechanisms (OAuth 2.0, JWT, API keys)
• Strong grasp of error handling, logging, and observability practices
• Experience building SDKs, libraries, or developer tools
• Knowledge of security best practices for API integrations and data handling
• Familiarity with Git, CI/CD pipelines, and deployment automation

• Hands‑on experience with Model Context Protocol (MCP) specification and implementations
• Experience integrating with LLM APIs (OpenAI, Anthropic, Azure OpenAI, Google Vertex AI)
• Understanding of AI agent frameworks (FastMCP)
• Knowledge of prompt engineering and LLM tool calling mechanisms
• Experience with function calling and structured output from LLMs
• Familiarity with enterprise platforms (Splunk, Databricks, Zendesk, Salesforce, Jira)
• Understanding of token optimization and context window management
• Experience with schema validation (JSON Schema, Pydantic, Zod)
• Knowledge of containerization (Docker) and orchestration (Kubernetes)
• Background in observability tools (Prometheus, Grafana, Datadog)
• Contributions to open‑source AI/LLM projects

• Protocols: JSON-RPC 2.0, REST, Graph
  
  QL, Server‑Sent Events (SSE), Web Sockets
• Data: JSON Schema, Pydantic models, data validation and serialization

• Understanding of AI agent architectures and multi‑agent systems
• Knowledge of LLM capabilities, limitations, and token economics
• Familiarity with prompt engineering and context optimization techniques
• Understanding of streaming responses and real‑time data handling
• Experience with callback mechanisms and event‑driven architectures
• Knowledge of data encryption and PII handling in AI contexts

• Strong problem‑solving ability with complex integration challenges
• Excellent written communication for documentation and tool descriptions
• Ability to design intuitive tool interfaces that LLMs can effectively use
• Collaborative mindset for working with AI engineers and product teams
• Attention to detail for schema design and error handling
• Proactive approach to monitoring and improving connector reliability
• Adaptability to rapidly evolving LLM and AI agent ecosystems

• Develop new MCP connectors for enterprise system integrations
• Debug tool calling issues and optimize parameter handling for LLM consumption
• Review and improve tool descriptions for better LLM understanding
• Implement rate limiting and error handling for production robustness
• Write unit tests and integration tests for connector reliability
• Monitor connector performance and troubleshoot agent workflow failures
• Collaborate with teams on new integration requirements
• Update connectors as upstream APIs change or LLM capabilities expand

• MCP servers exposing enterprise data and capabilities to AI agents
• Tool schemas and validation logic for safe LLM interactions
• Authentication and authorization layers for secure integrations
• Retry mechanisms and error recovery for resilient agent workflows
• Documentation and examples for connector usage
• Testing frameworks ensuring reliability across LLM interactions
• Monitoring and observability instrumentation for production systems