Software Engineer

Genesys•Indianapolis, IN

10d•$89,900 - $158,100•Hybrid

About The Position

At Genesys, we help organizations create better customer experiences through AI-powered experience orchestration. Our platform connects people, systems, data and AI to help organizations deliver more personalized service, improve operational efficiency and build stronger customer relationships. Help build, support and operate technology used by more than 8,000 organizations in over 100 countries – moving AI from possibility to production in real-world enterprise environments every day. Are you passionate about automation and building reliable backend solutions? Do you want to grow your skills on microservices that power CI/CD processes and cloud infrastructure at scale? At Genesys, we're looking for a Software Engineer to join our platform team and help drive automation, scalability, and developer experience — using both strong engineering fundamentals and modern AI-assisted development practices.

Requirements

3–5 years of experience in software engineering, with exposure to microservices or distributed systems and production support/maintenance.
Proficient in Python, with a focus on building backend applications and automation tooling.
Hands-on experience with cloud-based technologies (AWS preferred; Azure a bonus) and an interest in supporting development teams with robust backend solutions.
Familiar with CI/CD concepts and comfortable working with pipelines, automation tools, and version control systems.
Methodical when diagnosing issues and have a continuous improvement mindset.
Communicate clearly with teammates, ask good questions, and take ownership of your work while knowing when to escalate.
Practical, day-to-day experience using AI coding tools — such as Claude, Cursor, GitHub Copilot, or Kiro — for writing, reviewing, refactoring, or debugging code, and you apply them with critical judgment. You know when AI output needs verification, when it's a shortcut worth taking, and when it's not the right tool at all.
Worked with or are curious about agentic workflows (multi-step, tool-using AI agents that plan and execute tasks) and understand how they differ from single-shot AI code completion.

Nice To Haves

Demonstrated experience building and maintaining microservices in a CI/CD context.
Practical knowledge of cloud infrastructure and backend system development.
Understanding of distributed systems fundamentals and the challenges of scaling backend applications.
Familiarity with Agile methodologies and a commitment to applying best practices in automation and CI/CD.
Hands-on experience with AI-assisted development tools and agentic coding workflows, including any of the following: AI pair-programming / coding assistants (e.g., Claude, Cursor, GitHub Copilot, Kiro) Agentic development flows — using AI agents to plan, execute, and iterate on multi-step engineering tasks (e.g., scaffolding services, writing tests, running builds, opening PRs) Prompt engineering for code generation, review, and documentation Evaluating and validating AI-generated code for correctness, security, and maintainability before it ships
A clear-eyed approach to AI in engineering: leveraging it where it genuinely adds value and speed, while maintaining full engineering rigor and accountability for the output.
Willingness to learn, take feedback, and grow toward taking on broader scope over time.

Responsibilities

Implement microservices for CI/CD: Build and maintain backend microservices in Python that support CI/CD pipelines, enabling reliable and scalable deployment processes.
Contribute to CI/CD infrastructure: Develop APIs and automation components that improve deployment and testing environments, increasing speed and reliability across the development lifecycle.
Support system reliability: Monitor and maintain backend services, investigate issues, and contribute to the availability and performance of distributed systems.
Apply AI-assisted engineering practices: Use tools like Claude, Cursor, and Kiro to accelerate coding, code review, testing, and debugging — while applying sound engineering judgment about when AI output needs verification versus when it can be trusted as-is.
Collaborate and grow: Work closely with cross-functional teams, participate in code reviews, and continuously develop your technical skills with support from senior engineers.