Senior LLM Engineer (RAG & AWS Bedrock)
Tasks
- Build AI powered chatbot experiences using AWS Bedrock
- Build cloud native AI services using AWS
- Build scalable vector search solutions for enterprise knowledge retrieval
- Collaborate with engineering teams to improve AI performance scalability reliability
- Design and implement retrieval-augmented generation architectures
- Design and optimize embeddings and semantic search strategies
- Develop multi source data ingestion and retrieval pipelines
- Develop prompt engineering strategies to improve response quality and accuracy
- Evaluate new AI technologies and best practices for enterprise LLM applications
- Integrate structured and unstructured data sources into AI workflows
Perks/Benefits
- Career development
- English program
- Fitness membership
- Games and competitions
- International projects
- Remote work
Skills/Tech-stack
API Gateway | AWS | AWS Bedrock | Chatbot Development | ChromaDB | Embeddings | EventBridge | Faiss | Infrastructure as Code | Langchain | Language Models | Large Language Models | Llamaindex | OpenSearch | Pinecone | Prompt engineering | Python | Retrieval-Augmented Generation | S3 | SQS | Search strategies | Semantic Search | Semantic Search Strategies | Vector Search | Weaviate | “as-code”
Education
N/A
Roles
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