Python Backend Developer

– 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