{"schemaVersion":"jobsearcher.job.v1","id":"9990a9c927026798d342462b","url":"https://jobsearcher.com/jobs/9990a9c927026798d342462b","canonicalUrl":"https://jobsearcher.com/jobs/9990a9c927026798d342462b","title":"Python Backend Developer","description":"Qualifications\r\n4+ years of software development experience with production systems\r\nStrong proficiency in Python and/or TypeScript/Node.js\r\nDeep experience with REST APIs, GraphQL, and various integration patterns\r\nUnderstanding of JSON-RPC, WebSocket, or similar RPC protocols\r\nExpertise in async/await patterns and concurrent programming\r\nExperience with authentication mechanisms (OAuth 2.0, JWT, API keys)\r\nStrong grasp of error handling, logging, and observability practices\r\nExperience building SDKs, libraries, or developer tools\r\nKnowledge of security best practices for API integrations and data handling\r\nFamiliarity with Git, CI/CD pipelines, and deployment automation\r\nPreferred Qualifications\r\nHands-on experience with Model Context Protocol (MCP) specification and implementations\r\nExperience integrating with LLM APIs (OpenAI, Anthropic, Azure OpenAI, Google Vertex AI)\r\nUnderstanding of AI agent frameworks (FastMCP)\r\nKnowledge of prompt engineering and LLM tool calling mechanisms\r\nExperience with function calling and structured output from LLMs\r\nFamiliarity with enterprise platforms (Splunk, Databricks, Zendesk, Salesforce, Jira)\r\nUnderstanding of token optimization and context window management\r\nExperience with schema validation (JSON Schema, Pydantic, Zod)\r\nKnowledge of containerization (Docker) and orchestration (Kubernetes)\r\nBackground in observability tools (Prometheus, Grafana, Datadog)\r\nContributions to open-source AI/LLM projects\r\nTechnical Skills\r\nProtocols: JSON-RPC 2.0, REST, GraphQL, Server-Sent Events (SSE), WebSockets\r\nData: JSON Schema, Pydantic models, data validation and serialization\r\nDomain Knowledge\r\nUnderstanding of AI agent architectures and multi-agent systems\r\nKnowledge of LLM capabilities, limitations, and token economics\r\nFamiliarity with prompt engineering and context optimization techniques\r\nUnderstanding of streaming responses and real-time data handling\r\nExperience with callback mechanisms and event-driven architectures\r\nKnowledge of data encryption and PII handling in AI contexts\r\nSoft Skills\r\nStrong problem-solving ability with complex integration challenges\r\nExcellent written communication for documentation and tool descriptions\r\nAbility to design intuitive tool interfaces that LLMs can effectively use\r\nCollaborative mindset for working with AI engineers and product teams\r\nAttention to detail for schema design and error handling\r\nProactive approach to monitoring and improving connector reliability\r\nAdaptability to rapidly evolving LLM and AI agent ecosystems\r\nDay-to-Day Activities\r\nDevelop new MCP connectors for enterprise system integrations\r\nDebug tool calling issues and optimize parameter handling for LLM consumption\r\nReview and improve tool descriptions for better LLM understanding\r\nImplement rate limiting and error handling for production robustness\r\nWrite unit tests and integration tests for connector reliability\r\nMonitor connector performance and troubleshoot agent workflow failures\r\nCollaborate with teams on new integration requirements\r\nUpdate connectors as upstream APIs change or LLM capabilities expand\r\nWhat You'll Build\r\nMCP servers exposing enterprise data and capabilities to AI agents\r\nTool schemas and validation logic for safe LLM interactions\r\nAuthentication and authorization layers for secure integrations\r\nRetry mechanisms and error recovery for resilient agent workflows\r\nDocumentation and examples for connector usage\r\nTesting frameworks ensuring reliability across LLM interactions\r\nMonitoring and observability instrumentation for production systems\r\nJ-18808-Ljbffr","company":"Altius Technologies","rawCompany":"altius technologies","city":"New York","state":"NY","isRemote":false,"isActive":true,"createdAt":"2026-06-25T00:40:32.990Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1254.00","title":"Web Developers","slug":"web-developers"},{"code":"15-1251.00","title":"Computer Programmers","slug":"computer-programmers"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Python Backend Developer","description":"Qualifications\r\n4+ years of software development experience with production systems\r\nStrong proficiency in Python and/or TypeScript/Node.js\r\nDeep experience with REST APIs, GraphQL, and various integration patterns\r\nUnderstanding of JSON-RPC, WebSocket, or similar RPC protocols\r\nExpertise in async/await patterns and concurrent programming\r\nExperience with authentication mechanisms (OAuth 2.0, JWT, API keys)\r\nStrong grasp of error handling, logging, and observability practices\r\nExperience building SDKs, libraries, or developer tools\r\nKnowledge of security best practices for API integrations and data handling\r\nFamiliarity with Git, CI/CD pipelines, and deployment automation\r\nPreferred Qualifications\r\nHands-on experience with Model Context Protocol (MCP) specification and implementations\r\nExperience integrating with LLM APIs (OpenAI, Anthropic, Azure OpenAI, Google Vertex AI)\r\nUnderstanding of AI agent frameworks (FastMCP)\r\nKnowledge of prompt engineering and LLM tool calling mechanisms\r\nExperience with function calling and structured output from LLMs\r\nFamiliarity with enterprise platforms (Splunk, Databricks, Zendesk, Salesforce, Jira)\r\nUnderstanding of token optimization and context window management\r\nExperience with schema validation (JSON Schema, Pydantic, Zod)\r\nKnowledge of containerization (Docker) and orchestration (Kubernetes)\r\nBackground in observability tools (Prometheus, Grafana, Datadog)\r\nContributions to open-source AI/LLM projects\r\nTechnical Skills\r\nProtocols: JSON-RPC 2.0, REST, GraphQL, Server-Sent Events (SSE), WebSockets\r\nData: JSON Schema, Pydantic models, data validation and serialization\r\nDomain Knowledge\r\nUnderstanding of AI agent architectures and multi-agent systems\r\nKnowledge of LLM capabilities, limitations, and token economics\r\nFamiliarity with prompt engineering and context optimization techniques\r\nUnderstanding of streaming responses and real-time data handling\r\nExperience with callback mechanisms and event-driven architectures\r\nKnowledge of data encryption and PII handling in AI contexts\r\nSoft Skills\r\nStrong problem-solving ability with complex integration challenges\r\nExcellent written communication for documentation and tool descriptions\r\nAbility to design intuitive tool interfaces that LLMs can effectively use\r\nCollaborative mindset for working with AI engineers and product teams\r\nAttention to detail for schema design and error handling\r\nProactive approach to monitoring and improving connector reliability\r\nAdaptability to rapidly evolving LLM and AI agent ecosystems\r\nDay-to-Day Activities\r\nDevelop new MCP connectors for enterprise system integrations\r\nDebug tool calling issues and optimize parameter handling for LLM consumption\r\nReview and improve tool descriptions for better LLM understanding\r\nImplement rate limiting and error handling for production robustness\r\nWrite unit tests and integration tests for connector reliability\r\nMonitor connector performance and troubleshoot agent workflow failures\r\nCollaborate with teams on new integration requirements\r\nUpdate connectors as upstream APIs change or LLM capabilities expand\r\nWhat You'll Build\r\nMCP servers exposing enterprise data and capabilities to AI agents\r\nTool schemas and validation logic for safe LLM interactions\r\nAuthentication and authorization layers for secure integrations\r\nRetry mechanisms and error recovery for resilient agent workflows\r\nDocumentation and examples for connector usage\r\nTesting frameworks ensuring reliability across LLM interactions\r\nMonitoring and observability instrumentation for production systems\r\nJ-18808-Ljbffr","datePosted":"2026-06-25T00:40:32.990Z","dateModified":"2026-06-25T00:40:32.990Z","hiringOrganization":{"@type":"Organization","name":"Altius Technologies","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York","addressRegion":"NY","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"9990a9c927026798d342462b"},"url":"https://jobsearcher.com/jobs/9990a9c927026798d342462b"}}