Technical Lead

Navi Mumbai, Maharashtra

Job Summary

Fullstack python developer

Python 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 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 Energetic 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"

Skill Requirements

Other Requirements

📌 Technical Lead (India)
🏢 HCLTech
📍 India