Software Engineer (AI Focused)

Assurant•Atlanta, GA

1d•$74,600 - $124,700•Hybrid

About The Position

Assurant is looking for a Software Engineer with an AI focus to help build and ship AI-powered products in production. In this role you will implement features across the AI stack — model integration, retrieval pipelines, evaluation, and the services that support them — working alongside senior engineers who will help you grow into deeper technical ownership over time. This is a hybrid role base out of our Atlanta or Miami office.

Requirements

2+ years of professional software engineering experience building and shipping production software.
Proficiency in at least one modern programming language (e.g., Python, TypeScript, Java, Go, C#).
Hands-on experience integrating LLM APIs into an application — prompting, structured outputs, and function/tool calling — through professional work, internships, or substantive personal projects.
Working knowledge of core backend fundamentals: REST APIs, databases, version control, testing, and debugging.
Curiosity about how AI systems behave in the real world, and a willingness to measure results rather than assume them.
Clear written and verbal communication, and comfort asking for help and giving updates.

Nice To Haves

Familiarity with AI orchestration frameworks such as Semantic Kernel, LangGraph, or LangChain.
Exposure to vector databases, embedding models, or semantic search.
Experience with a major cloud platform (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
Familiarity with CI/CD pipelines, monitoring, and observability tooling.
Experience using AI-assisted development tools such as Claude Code, Cursor, or equivalent.
Awareness of responsible AI practices including safety testing, content filtering, and bias considerations.
Open-source contributions, technical writing, or side projects involving LLMs.

Responsibilities

Build, test, and ship features for production AI systems, including retrieval-augmented generation (RAG) pipelines, agentic workflows, and model orchestration components.
Write and maintain evaluation cases and test suites that catch regressions in AI system quality before they reach users.
Implement and iterate on prompts, tool/function definitions, and guardrails, using eval results to guide changes rather than intuition alone.
Contribute to the services, APIs, and data pipelines that AI features depend on, including instrumentation, logging, and dashboards.
Investigate production issues — latency spikes, cost anomalies, quality regressions, failed tool calls — and implement fixes.
Participate in code review, design discussions, and post-incident analyses, asking questions and sharing what you learn.
Work closely with Product, Design, and Data Science teammates to turn requirements into working software and to flag tradeoffs early.
Keep documentation and runbooks current for the systems you work on.