Job Description & Details
This is a heavy-hitting technical role bridging full-stack software engineering with modern generative AI architectures. You aren't just pushing web UI updates or tweaking basic prompts; you are building production-grade GenAI pipelines, RAG systems, and cloud-native microservices that handle complex enterprise workloads. If you enjoy jumping between front-end frameworks like Angular or React and back-end orchestration using LangChain, Python, or .NET while leading technical workstreams, this is worth a close look.
What You'll Actually Be Doing
Your day-to-day will be a mix of designing robust system architectures, writing clean code across the stack, and unblocking your team on thorny technical challenges. You'll spend significant time wiring up LLM integrations—whether OpenAI, Anthropic, or open-source weights—into scalable RAG pipelines and vector databases hosted on AWS, Azure, or GCP. Beyond raw coding, you'll be the technical anchor establishing engineering standards, driving DevSecOps practices, and mentoring junior devs on everything from clean OOP design to AI-augmented development specs.
The Core Tech Stack
You need a polyglot background that blends traditional enterprise engineering with cutting-edge AI. On the web side, fluency in Angular, React, Node.js, Python, C#, or Java is mandatory. But the real meat of the role requires deep, hands-on familiarity with PyTorch, TensorFlow, LangChain, LangGraph, and vector databases. Add in cloud hyperscalers like Azure OpenAI, AWS Bedrock, or Vertex AI, plus a strong grasp of containerized microservices and automated unit testing, and you've got the exact toolkit this team relies on daily to ship fast without breaking things.
Interview Expectations
Expect the interviewers to drill deep into your system design capabilities, particularly around state management and latency optimization in RAG pipelines. A classic question you will likely face is: 'Walk me through how you would architect a fault-tolerant RAG system that minimizes hallucination while ingesting streaming enterprise documents.' The hiring manager is looking to see if you understand the actual bottlenecks of vector search, context window limits, and fallback strategies, rather than just reciting high-level definitions. Another technical probe will likely focus on multi-agent orchestration, where they'll ask how you handle state management, loops, and failure recovery when using tools like LangGraph or agentic frameworks.
Application Advice
To get past the ATS, your resume needs to explicitly map your past projects to the exact toolchains mentioned in the spec. Don't just list 'Python' or 'React'—write bullet points highlighting specific instances where you built RAG architectures, integrated specific LLMs via APIs, or deployed cloud-native microservices using FaaS/PaaS on Azure, AWS, or GCP. Emphasize any experience you have leading technical workstreams or mentoring engineers, as this role carries a strong leadership and standard-setting component alongside the heavy coding.