Python Backend Developer

– Strong proficiency in Python and/or TypeScript/Node.js – Deep experience with REST APIs, GraphQL, and various integration patterns – Understanding of JSON-RPC, WebSocket, 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 Preferred Qualifications – 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 Technical Skills – Languages: Python 3.10+, JavaScript (Node.js 18+) – Protocols: JSON-RPC 2.0, REST, GraphQL, Server-Sent Events (SSE), WebSockets – LLM Integration: OpenAI API, Anthropic Claude API, Azure OpenAI, function calling, tool use – Frameworks: FastAPI, Express.js, async/await patterns, Agent SDK integration – Data: JSON Schema, Pydantic models, data validation and serialization – Tools: Git, Docker, pytest, Jest, VS Code, Postman/Insomnia – Security: OAuth 2.0, JWT, encryption (AES, RSA), secure secret management – Concepts: API design, rate limiting, retry logic, circuit breakers, idempotency Domain Knowledge – 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 Soft Skills – 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 Day-to-Day Activities – 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 What You’ll Build – 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