Mumbai, Maharashtra
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)
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