Job Description & Details
This is a legitimate ground-floor opportunity to build an enterprise-grade GenAI platform rather than just slapping a thin wrapper over an LLM API. You will be knee-deep in production challenges like reducing hallucinations, optimizing vector search, and wiring up agentic workflows that actually behave predictably in a business environment. If you like solving messy integration problems instead of just playing with cool tech demos, this role will keep you busy.
What You'll Actually Be Doing
You will spend your days architecting and writing code for end-to-end AI pipelines, starting from document ingestion and chunking all the way to complex RAG and multi-step agent execution. Expect to spend a lot of time refining prompt engineering, setting up robust guardrails to catch bad outputs before they hit users, and dealing with the nuances of hybrid search. You will work closely with product and architecture teams to ensure the applications scale, remain secure, and actually drive measurable business value instead of creating expensive hallucinations.
The Core Tech Stack
Python is your primary weapon here, and you'll need solid production experience with it alongside REST APIs and relational databases (SQL). The heavy lifting revolves around LLMs, vector databases, and frameworks like LangChain, LangGraph, or Semantic Kernel. You must understand the mechanics of embeddings, semantic search, and vector math because tuning a RAG pipeline or building agentic tool-calling workflows requires knowing what is happening under the hood when queries fail or latency spikes.
Interview Expectations
Expect the hiring team to hand you a scenario where a RAG pipeline is spitting out inaccurate answers or taking four seconds too long to respond, and they will want to know how you debug it. They are secretly testing whether you understand token limits, embedding drift, and reranking strategies or if you just know how to copy-paste documentation. They will also likely push you on agent state management—specifically, how you handle failure states, retries, and human-in-the-loop validation when an autonomous agent tries to call an external API and fails.
Application Advice
Skip the generic tech buzzwords and make sure your resume explicitly highlights production-level experience with Python, vector databases, and LLM orchestration frameworks. If you have built custom RAG pipelines that handle messy enterprise data like PDFs or spreadsheets, bring those details front and center. Use keywords like Retrieval-Augmented Generation, vector search, semantic chunking, prompt engineering, and agentic workflows to ensure you clear the ATS filters and catch the eye of the engineering leads reviewing the stack.