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Description supplied by the original job listing.
Responsibilities:
Design, build, and own GenAI features for sales intelligence products (AMA, AI Playbook Automation).
Develop and optimize prompt templates and chains for LLM applications, ensuring high-quality, reliable outputs.
Implement RAG (Retrieval Augmented Generation) pipelines with vector search and semantic retrieval.
Build and maintain integrations with LLM APIs (OpenAI, Anthropic, etc. ) and orchestration frameworks.
Own AI system performance monitoring using observability tools (Langfuse, etc. ).
Collaborate on system architecture decisions and technical tradeoffs for AI features.
Deploy and maintain AI services in production (Kubernetes, FastAPI backends).
Evaluate and improve LLM outputs through systematic testing and iteration.
Requirements:
5-6 years of experience in software engineering with a strong focus on GenAI/LLM applications.
Bachelor's or master's degree in computer science from a top-tier university.
Hands-on experience building production LLM applications and prompt engineering.
Proficiency in Python with experience in FastAPI, Django, or similar frameworks.
Working knowledge of vector databases (Pinecone, Weaviate, Qdrant, Chroma) and embedding models.
Experience with LLM orchestration frameworks (LangChain, LangGraph, or similar).
Understanding of RAG architectures and semantic search.
Familiarity with Docker, Kubernetes, and cloud platforms (AWS/GCP).
Strong problem-solving skills and ability to work independently with ownership.
Experience
4-6 yrs
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