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Tech Stack
Tools & technologiesAWSEC2Python
About the role
Key responsibilities & impact- Advanced RAG Architecture: Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, focusing on retrieval quality and context relevance.
- Agent Orchestration: Build complex cognitive flows and decision graphs using LangChain and LangGraph.
- Retrieval Engineering: Manage and optimize vector databases (with emphasis on LanceDB), ensuring efficient searches via embeddings.
- Model Integration: Implement and fine-tune integrations with cutting-edge LLMs (OpenAI, Claude, Mistral), balancing cost, latency, and quality.
- AI Infrastructure: Maintain scalable solutions on AWS, using Lambdas for asynchronous processing and S3 for large data volumes.
- Strategic Evolution: Translate business needs into viable AI solutions and actively contribute to defining the team's technical roadmap.
Requirements
What you’ll need- Strong command of Python (focus on performant, clean code).
- Hands-on, proven experience with LLMs and prompt engineering techniques.
- Solid knowledge of LangChain and LangGraph (essential for stateful and cyclical flows).
- Experience with vector databases (LanceDB is a strong plus).
- Familiarity with AWS compute and storage services (Lambda, S3, EC2).
- Strong academic background (Computer Science, Engineering, Mathematics, or related fields).
Benefits
Comp & perks- Paid vacation after 12 months of employment.
- Starbem
- TotalPass
ATS Keywords
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Hard Skills & Tools
PythonRetrieval-Augmented Generation (RAG)LangChainLangGraphvector databasesLanceDBLLMsprompt engineeringAWSasynchronous processing
Soft Skills
strategic thinkingproblem-solvingcommunicationcollaborationtechnical roadmap development
