Job SummaryFullstack python developerPython GenAI Engineer with experience in developing AI-powered applications using Large Language Models (LLMs), RAG architectures, and Agentic AI frameworks. The role involves designing scalable AI solutions, building and optimizing retrieval pipelines, implementing vector search capabilities, and developing intelligent applications using frameworks such as LangChain and LangGraph.Key Responsibilities"Key Responsibilities➢Design and develop scalable applications using Python➢Implement and maintain AI-powered features using Large Language Models (LLMs) and agentic AI systems➢Build and optimize RAG (Retrieval Augmented Generation) pipelines➢Create and maintain vector databases for effective similarity search and document retrieval➢Develop and optimize embedding systems for text and data processing➢Set up and manage monitoring dashboards using Grafana➢Design and implement efficient data ingestion and processing pipelines➢Collaborate with cross-functional teams to deliver intelligent software solutions➢Participate in code reviews and contribute to technical documentation➢Optimize application performance and troubleshoot production issuesRequired Skills & Experience1.3-5 years of professional software development experience2.Strong proficiency in Python3.Advanced Python development skills, including experience with:oLangChain LangGraph or similar LLM frameworksoHugging Face transformersoVector databases (Qdrnt, Weaviate, or similar)oEmbedding 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-ranking5.Knowledge of modern AI/ML concepts and applications6.Experience with graph databases (Neo4j, Amazon Neptune)7.Hands-on experience with Grafana for monitoring and visualization8.Strong knowledge of SQL, NoSQL ,MySqldatabases9.Proficiency with version control systems (Git),AWS,Data governance.Typescript/java scriptPreferred Skills
- Experience with:oAI agents and autonomous systemsoSemantic search implementationsoKnowledge graphs and ontologiesoStream processing for real-time AI applications
- Containerization (Docker, Kubernetes)
- Message queuing systems (Kafka, RabbitMQ)
- CI/CD pipelines
- Prometheus or other monitoring solutions
- MLOps practices and tool"Skill RequirementsOther Requirements

📌 Technical Lead (Thane)
🏢 HCL
📍 Thane