> Markdown version of [/jobs/ext/4296354-senior-software-engineer](https://www.wearedevelopers.com/jobs/ext/4296354-senior-software-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior Software Engineer

- **Company:** Brandt
- **Location:** Tallahassee, FL, United States (Remote available)
- **Experience:** Expert
- **Salary:** $130,000.0 - $150,000.0
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Code Review, Continuous Integration, Information Leak Prevention, Relational Databases, DevOps, Software Engineering, Value Engineering, Google Cloud, Cloud Platform System, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, AI Coding Agents, Containerization, Kubernetes, Front End Software Development, Prompt Injection, Restful APIs, Model Context Protocol, Docker, Programming Languages, Microservices
- **Published:** October 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=01169c1e37491b46

## About the Role

* 7+ years of hands-on software engineering experience, including ownership of significant components or features from design through production
 * You work in a highly agentic way today. AI coding agents are central to how you build, and you know when to trust their output and when to verify it yourself
 * You've built your own agents, automations, or tooling to make yourself or your team faster, such as custom agent workflows, MCP servers, or scripts that chain models and tools together. Side projects and internal tools count
 * Working knowledge of how agents work under the hood: tool use, orchestration, context management, and structured outputs
 * A habit of checking AI output rather than assuming it's right, whether through tests, review, or lightweight evals
 * Awareness of AI security and data-handling risks, including prompt injection, data leakage, and access control for agents
 * Strong foundation in at least one modern programming language and the ability to pick up others quickly; we value engineering depth and fast learning over loyalty to a specific stack
 * RESTful APIs, service-oriented or microservice architectures, and at least one major cloud platform (e.g., Azure, AWS, GCP)
 * Relational databases and data modeling
 * DevOps practices and CI/CD tooling, with experience automating build, test, and release pipelines
 * Experience working in, or integrating with, legacy and long-lived systems
 * Nice to have: Experience building LLM-powered features into products, including retrieval-augmented generation, evals, production monitoring of AI behavior, and vector or search stores
 * Nice to have: containerization (Docker, Kubernetes), Model Context Protocol (MCP) or similar tool-integration standards, front-end experience with a modern framework, contributions to open-source projects or technical writing
 * No specific industry background is required. We care how quickly you learn a new domain, not which one you came from

## Description

This is a senior individual contributor role, for engineers who want to stay close to the code and own meaningful pieces of the work from design through production. You're not here to manage a team; we have dedicated Engineering Managers and Tech Leads for that. You're here to design and build high-leverage systems yourself, including the AI-enabled systems and agents that change how our teams work, within the architecture and standards the rest of engineering builds on., * Design, develop, and deliver complex technical solutions, including AI-enabled and agentic systems, that meet functional, performance, and security requirements

 * Build production-grade agents and AI-powered workflows, including tool use, orchestration, retrieval, and integration with existing systems of record
 * Translate loosely defined business and product requirements into a clear technical plan and execute against it
 * Write high-quality, maintainable, testable code, and own the components you build from design through production
 * Use AI coding assistants and agentic coding tools daily, apply the team's AI practices, and bring back what works and what doesn't so those practices improve
 * Build evaluation, testing, and observability for the AI behavior you ship so quality is measured rather than assumed
 * Apply AI across the delivery lifecycle, from discovery and requirements through build, test, release, and support, and help measure the effect on speed and quality
 * Build safe interfaces, adapters, and guardrails that let agents work against legacy and long-lived systems
 * Participate in code reviews and design reviews, giving and receiving feedback that raises the quality of the team's work
 * Collaborate across product, architecture, QA, and DevOps to ensure end-to-end delivery quality, and uphold strong engineering standards and CI/CD discipline
 * Identify technical risks and dependencies early and raise them, including AI-specific risks such as security, data privacy, cost, and reliability
 * Evaluate emerging models, tools, and techniques and recommend what fits the problem, staying model- and vendor-agnostic
 * Work across product domains (Licensing, Registrations, Reservations, and beyond) as business priorities shift