Python Senior Developer - Data Analysis, SQL

Job Summary

Required Skills & Experience 2) 3-5 years of professional software development experience 2. Strong proficiency in Python 3. Advanced Python development skills, including experience with: o LangChain LangGraph or similar LLM frameworks o Hugging Face transformers o Vector databases (Qdrnt, Weaviate, or similar) o Embedding models (OpenAI, BERT, or similar) 4. Experience implementing RAG architecture or having Knowledge on any of the below ▪ Basic RAG Implementation: ▪ Document chunking and preprocessing ▪ Embedding generation and storage ▪ Vector similarity search ▪ LLM prompt engineering and context injection ▪ Hybrid RAG Architectures: ▪ Keyword-based + Dense / Sparse Vector Retrieval ▪ BM25 + Neural Search combinations ▪ Multi-index retrieval strategies ▪ Hybrid re-ranking approaches ▪ Advanced RAG Patterns: ▪ Parent-Child Document Chunking ▪ Recursive Retrieval ▪ Multi-Query RAG ▪ Hypothetical Document Embeddings (HyDE) ▪ Query Decomposition ▪ Self-Query RAG ▪ RAG Pipeline Components: ▪ Document Loaders and Parsers ▪ Text Splitters (Recursive, Semantic, Token-based) ▪ Embedding Models Integration ▪ Vector Store Operations ▪ Query Routing and Processing ▪ Response Generation and Synthesis ▪ RAG Enhancement Techniques: ▪ Auto-merging Retrieved Chunks ▪ Semantic Router Implementation ▪ Context Window Optimization ▪ Query Expansion Strategies ▪ Re-ranking Mechanisms ▪ Sentence Window Retrieval ▪ Advanced Retrieval Methods: ▪ Multi-Vector Retrieval ▪ Time-Weighted Retrieval ▪ Contextual Compression ▪ Dynamic Few-Shot Learning ▪ Cross-Encoder Re-ranking 5. Knowledge of modern AI/ML concepts and applications 6. Experience with graph databases (Neo4j, Amazon Neptune) 7. Hands-on experience with Grafana for monitoring and visualization 8. Strong knowledge of SQL and NoSQL databases 9. Proficiency with version control systems (Git)

Key Responsibilities

Required Skills & Experience 2) 3-5 years of professional software development experience 2. Strong proficiency in Python 3. Advanced Python development skills, including experience with: o LangChain LangGraph or similar LLM frameworks o Hugging Face transformers o Vector databases (Qdrnt, Weaviate, or similar) o Embedding models (OpenAI, BERT, or similar) 4. Experience implementing RAG architecture or having Knowledge on any of the below ▪ Basic RAG Implementation: ▪ Document chunking and preprocessing ▪ Embedding generation and storage ▪ Vector similarity search ▪ LLM prompt engineering and context injection ▪ Hybrid RAG Architectures: ▪ Keyword-based + Dense / Sparse Vector Retrieval ▪ BM25 + Neural Search combinations ▪ Multi-index retrieval strategies ▪ Hybrid re-ranking approaches ▪ Advanced RAG Patterns: ▪ Parent-Child Document Chunking ▪ Recursive Retrieval ▪ Multi-Query RAG ▪ Hypothetical Document Embeddings (HyDE) ▪ Query Decomposition ▪ Self-Query RAG ▪ RAG Pipeline Components: ▪ Document Loaders and Parsers ▪ Text Splitters (Recursive, Semantic, Token-based) ▪ Embedding Models Integration ▪ Vector Store Operations ▪ Query Routing and Processing ▪ Response Generation and Synthesis ▪ RAG Enhancement Techniques: ▪ Auto-merging Retrieved Chunks ▪ Semantic Router Implementation ▪ Context Window Optimization ▪ Query Expansion Strategies ▪ Re-ranking Mechanisms ▪ Sentence Window Retrieval ▪ Advanced Retrieval Methods: ▪ Multi-Vector Retrieval ▪ Time-Weighted Retrieval ▪ Contextual Compression ▪ Dynamic Few-Shot Learning ▪ Cross-Encoder Re-ranking 5. Knowledge of modern AI/ML concepts and applications 6. Experience with graph databases (Neo4j, Amazon Neptune) 7. Hands-on experience with Grafana for monitoring and visualization 8. Strong knowledge of SQL and NoSQL databases 9. Proficiency with version control systems (Git)

Skill Requirements

Required Skills & Experience 2) 3-5 years of professional software development experience 2. Strong proficiency in Python 3. Advanced Python development skills, including experience with: o LangChain LangGraph or similar LLM frameworks o Hugging Face transformers o Vector databases (Qdrnt, Weaviate, or similar) o Embedding models (OpenAI, BERT, or similar) 4. Experience implementing RAG architecture or having Knowledge on any of the below ▪ Basic RAG Implementation: ▪ Document chunking and preprocessing ▪ Embedding generation and storage ▪ Vector similarity search ▪ LLM prompt engineering and context injection ▪ Hybrid RAG Architectures: ▪ Keyword-based + Dense / Sparse Vector Retrieval ▪ BM25 + Neural Search combinations ▪ Multi-index retrieval strategies ▪ Hybrid re-ranking approaches ▪ Advanced RAG Patterns: ▪ Parent-Child Document Chunking ▪ Recursive Retrieval ▪ Multi-Query RAG ▪ Hypothetical Document Embeddings (HyDE) ▪ Query Decomposition ▪ Self-Query RAG ▪ RAG Pipeline Components: ▪ Document Loaders and Parsers ▪ Text Splitters (Recursive, Semantic, Token-based) ▪ Embedding Models Integration ▪ Vector Store Operations ▪ Query Routing and Processing ▪ Response Generation and Synthesis ▪ RAG Enhancement Techniques: ▪ Auto-merging Retrieved Chunks ▪ Semantic Router Implementation ▪ Context Window Optimization ▪ Query Expansion Strategies ▪ Re-ranking Mechanisms ▪ Sentence Window Retrieval ▪ Advanced Retrieval Methods: ▪ Multi-Vector Retrieval ▪ Time-Weighted Retrieval ▪ Contextual Compression ▪ Dynamic Few-Shot Learning ▪ Cross-Encoder Re-ranking 5. Knowledge of modern AI/ML concepts and applications 6. Experience with graph databases (Neo4j, Amazon Neptune) 7. Hands-on experience with Grafana for monitoring and visualization 8. Strong knowledge of SQL and NoSQL databases 9. Proficiency with version control systems (Git)

Other Requirements