Job Description & Details
This is a solid mid-level engineering gig focused on bridging traditional software development with modern GenAI and cloud-native architectures. You will be building real production-grade AI features rather than just stitching together simple wrapper scripts, meaning you need a solid foundation in software engineering principles alongside your machine learning interests.
What You'll Actually Be Doing
You'll spend your days designing, building, and deploying AI-infused applications, which basically means working on RAG pipelines, hooking up LLMs like OpenAI or Anthropic, and integrating vector databases into microservices. You won't just write code and throw it over the wall; you will be collaborating closely with team members, ensuring your work follows best practices, and helping to untangle complex business requirements into clean system designs.
The Core Tech Stack
You need a strong foundation in a modern backend or full-stack language like Python, C#, .NET, Java, NodeJS, or TypeScript with Angular/React, paired with actual hands-on experience in the GenAI ecosystem. Specifically, familiarity with PyTorch, TensorFlow, LangChain, LangGraph, and cloud hyperscalers like Azure, AWS, or GCP (using services like AWS Bedrock or Azure OpenAI) is non-negotiable because the team relies heavily on these tools to scale their intelligent applications.
Interview Expectations
Expect the interviewers to grill you on how you handle latency and hallucinations when building production Retrieval-Augmented Generation (RAG) pipelines. They want to know that you understand chunking strategies, embedding models, and vector database retrieval performance, rather than just knowing how to call an API. They will also likely test your core computer science fundamentals, OOP principles, and system design capability using sequence or data flow diagrams.
Application Advice
To get past the ATS for this role, make sure your resume explicitly highlights your hands-on experience with GenAI tools, vector databases, and cloud services like AWS Bedrock or Azure OpenAI, rather than just listing general software engineering buzzwords. Quantify your impact on past projects where you deployed microservices or integrated AI models, and make sure your tech stack section explicitly includes keywords like LangChain, Python, or .NET to match their requirements.