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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer (Python) - Remote - **Company:** Mitratech - **Location:** Austin, TX, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon Cloudfront, Amazon S3, Automation of Tests, Software as a Service, Code Generation, Code Review, Information Systems, Continuous Integration, DevOps, Amazon DynamoDB, Python (Programming Language), Open Web Application Security, OpenAI, Search Technologies, Secure Coding, Software Engineering, Strategies of Testing, ReactJS, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Claude Code, Multi-Agent Systems, Prompt Engineering, Software Security, AI Coding Agents, Agentic-AI, Containerization, Information Technology, Amazon Bedrock, Virtual Agents, Functional Programming, Invoking Functions, Api Gateway, Restful APIs, Amazon Simple Queue Service (SQS), Service Stack - **Published:** November 6, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/88793404/1 ## About the Role * Possess a bachelor's degree, or equivalent experience. Emphasis in engineering, data science, computer science or information systems is preferred. * Have a minimum of 5 years of software engineering experience developing SaaS applications, with strong hands-on experience in our stack: Python, React, AWS (Lambda, DynamoDB). * Hands-on experience with LLM application patterns: prompt design, embeddings and vector search/RAG, tool/function calling, structured outputs, and agent orchestration. * Demonstrated experience delivering software with AI coding agents such as Claude Code, Devin, or Codex as a core part of your workflow, including integrating agents into CI/CD pipelines with automated quality gates. * Experience designing evaluation and testing strategies for non-deterministic systems, and the judgment to decide when AI is, and is not, the right solution. * Strong fundamentals in system design, API security, automated testing, and multi-tenant SaaS architecture. * Clear, precise written communication: specs and documentation that both people and AI agents can act on. Preferred * Experience with managed model platforms and provider APIs (e.g., Amazon Bedrock, Anthropic, OpenAI) and agent frameworks or SDKs. * Have shipped at least one AI/LLM-powered feature to production in a SaaS product and supported it after launch (monitoring, evaluation, and iteration based on real usage). * Experience building MCP servers or other tool integrations for AI agents. * Background in regulated domains (HR, legal, risk, or compliance) and with data privacy and security standards (e.g., SOC 2, GDPR, CCPA). * Familiarity with AI risk and governance practices (e.g., NIST AI RMF, OWASP Top 10 for LLM Applications). * Experience with spec-driven development. Our Technology Stack: * Python, React * AWS (Lambda, DynamoDB, SQS, S3, CloudFront, API Gateway) * Containerized environments with CICD pipelines, RESTful APIs, etc. * LLM, RAG, and MCP components powering in-product AI features * AI coding agents integrated into the development lifecycle with automated quality gates ## Description Agentic, Pipeline-Driven Development * System Design & Task Decomposition: Break down high-level product goals into well-defined, modular engineering tasks with clear specifications and acceptance criteria that can be executed by both engineers and AI coding agents. * Agentic Development Pipelines: Develop and optimize CI/CD pipelines that incorporate AI agents for code generation, automated testing, and intelligent code review, with guardrails, quality gates, and clear human approval points. * Specification & Context Engineering: Design and test structured instructions, prompts, specifications, and repository context (e.g., agent instruction files, reusable skills, MCP tools) that drive accurate, context-aligned output from AI agents, and measure and improve agent output over time. * Code Review & Validation: Conduct in-depth reviews of both human and AI-generated code, ensuring quality, security, scalability, and alignment with domain requirements. * Architectural Oversight: Ensure design plans created by both human engineers and AI tools adhere to architectural best practices with a focus on modularity, bounded contexts, etc. * Documentation and Domain Clarity: Contribute to the development of domain models, technical documentation, and internal knowledge bases that support AI agent performance and team activity. Engineering Excellence & Teamwork * Write secure code. Avoid introducing an OWASP vulnerability, including those in the OWASP Top 10 for LLM Applications. Know how APIs are secured, sessions are handled, and data is validated properly. * Serve in an on-call rotation (DevOps philosophy where you will participate in ops) and respond promptly to any production issues, including AI feature degradations, to remediate rapidly. * Be a teammate, help other teammates resolve complex problems or think through design/architecture challenges. Be available and responsive. * Mentor others and foster an excellent engineering culture based in trust, accountability, character, and honesty, including coaching teammates on using AI agents effectively and responsibly. * Seek feedback and deliver constructive criticism (e.g., code reviews) in a tactful and empathetic manner; build others up and mentor without ego.