Job Description & Details
This is a heavy-hitting data role that blends traditional engineering with bleeding-edge Generative AI development. If you want to move beyond basic reporting and actually build production-grade LLM applications on AWS, this hybrid gig in Reston is worth a close look.
What You'll Actually Be Doing
Expect to spend your days knee-deep in data pipelines, figuring out how to feed messy corporate data into advanced AI systems. You will be designing scalable architectures, spinning up RAG workflows, and integrating REST APIs to connect modern LLM agents with core enterprise systems. The team needs someone who can operate autonomously, turning vague business requirements into concrete cloud-native solutions without needing constant micromanagement.
The Core Tech Stack
You are going to live and breathe Python and PySpark for heavy data processing, alongside AWS services like Lambda, Glue, and Amazon Bedrock for the AI side. Strong SQL and data modeling fundamentals are non-negotiable because the flashiest Generative AI tool fails if your underlying data architecture is a mess. You also need solid experience building and consuming REST APIs to tie these complex, multi-layered workflows together.
Interview Expectations
Expect the hiring team to grill you on how you handle data scaling bottlenecks when running PySpark jobs alongside AWS serverless components. They want to know you understand the real-world latency and cost traps of Amazon Bedrock and RAG architectures, rather than just knowing the buzzwords. Be ready to whiteboard a complete data pipeline from a raw relational database to an AI agent endpoint.
Application Advice
Don't just list Python and AWS on your resume and hope for the best; explicitly highlight projects where you built end-to-end data pipelines or integrated LLMs into existing products. Make sure keywords like PySpark, Amazon Bedrock, RAG, and REST API are prominent in your bullet points to ensure you sail past the automated filters.