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Tech Stack
Tools & technologiesETLNoSQLPythonSQL
About the role
Key responsibilities & impact- Design and maintain robust data ingestion and transformation pipelines tailored for LLM training, fine-tuning, and Retrieval-Augmented Generation (RAG)
- Utilize LangGraph to develop complex, state-managed AI agents and cyclical workflows
- Architect the retrieval layer of our AI applications, implementing efficient document embedding strategies and semantic search
- Implement and optimize Vector Databases (e.g., Pinecone, Weaviate, or Milvus)
- Create scalable data schemas that support both structured and unstructured data
- Identify and resolve latency bottlenecks in data retrieval and embedding generation
- Collaborate with AI Researchers and Product Managers to transition experimental AI prototypes into production-ready data products
Requirements
What you’ll need- 2+ years of professional experience in data engineering or backend-heavy software engineering role
- Expert-level Python coding skills (Must Have)
- Deep, hands-on experience (Must Have) with LangChain or LangGraph
- Proven experience (Must Have) implementing and tuning Vector Databases for high-volume RAG pipelines
- Strong understanding of traditional data modeling, ETL/ELT processes, and working with SQL/NoSQL databases
- Solid grasp of embedding models, tokenization, and modern information retrieval techniques
- Thrive in fast-paced environments and enjoy staying updated with the rapidly evolving landscape of GenAI and search technologies.
Benefits
Comp & perks- Professional development opportunities
- Inclusive workplace that ignites growth
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
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Hard Skills & Tools
PythonLangGraphLangChainVector DatabasesETLELTSQLNoSQLdata modelingembedding models
Soft Skills
collaborationproblem-solvingadaptabilitycommunicationtime management