– 4+ years of software development experience with production systems
– 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