Technical Lead

"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 efficient 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 issues Required Skills & Experience 1. 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, NoSQL ,MySqldatabases 9. Proficiency with version control systems (Git),AWS,Data governance.Typescript/java script Preferred Skills • Experience with: o AI agents and autonomous systems o Semantic search implementations o Knowledge graphs and ontologies o Stream 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"