Job Description & Details
This is a heavy-hitting architecture and full-stack role based out of Plano, TX, requiring you to be on-site five days a week. You won't just be writing boilerplate code; you'll be designing and shipping enterprise-grade GenAI systems that tie together modern cloud infrastructure and heavy backend data pipelines.
What You'll Actually Be Doing
You will spend your days bridging the gap between traditional enterprise software development and cutting-edge generative AI. Expect to architect scalable Java and Python microservices, wire up RAG pipelines using LangChain and AWS Bedrock, and ensure the React frontends stay snappy and responsive. Half your time will go into system design and data modeling, while the rest involves fighting Kubernetes clusters, setting up Kafka topics, and unblocking junior engineers who are trying to wrap their heads around cloud-native deployments.
The Core Tech Stack
To survive here, you need deep mastery of Java, Python, and the Spring framework for your backend foundations. On the frontend, React is non-negotiable. However, the real differentiator for this role is your cloud and AI toolbelt: AWS services, Docker, Kubernetes, Terraform, and Ansible are table stakes, while hands-on experience with LangChain, LangGraph, and vector databases or RAG architectures will determine whether you can actually deliver on their GenAI roadmap.
Interview Expectations
Expect the engineering panel to drill down into your distributed systems knowledge, likely asking you to design a fault-tolerant RAG pipeline that can handle massive concurrent loads without choking AWS Bedrock. The interviewer wants to see if you can anticipate bottleneck issues in event-driven streaming with Kafka and whether you actually know how to debug memory leaks in a containerized Java microservice, rather than just reciting textbook definitions.
Application Advice
Your resume needs to heavily feature your architectural contributions, specifically highlighting systems where you owned both the backend codebase and the infrastructure-as-code deployments. Make sure terms like LangChain, Kubernetes, Terraform, Kafka, and AWS Bedrock are explicitly mentioned in your experience bullets so you easily clear the ATS filters and catch the hiring manager's eye.