Python Developer

Python Developer

Hybrid - Alpharetta, GA (3 days/week onsite)

12 Months

W2 Only!!

Key Responsibilities

• Design and develop MCP server implementations exposing tools, resources, and prompts for LLM consumption
• Build secure connectors integrating enterprise platforms (Splunk, Zendesk, Databricks, ServiceNow, etc.) with AI agent systems
• Implement JSON-RPC protocol handlers and manage client-server communication patterns
• Develop tool schemas and parameter validation logic for LLM tool calling
• Create error handling, retry mechanisms, and circuit breakers for production reliability
• Optimize connector performance for low-latency AI agent interactions
• Implement authentication flows (OAuth, API keys, SSO) and secure credential management
• Write comprehensive tool documentation including descriptions, parameters, and usage examples that LLMs can understand
• Build testing frameworks for connector reliability and LLM interaction validation
• Collaborate with AI/ML engineers to understand agent requirements and context limitations
• Monitor connector usage patterns and optimize for token efficiency
• Maintain and version existing MCP servers as APIs and LLM capabilities evolve

Required Qualifications

• 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