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.
Solid 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"

Skill Requirements

Must Have Skill

Amazon Web Services (AWS),Python,TypeScript,Data Governance,Generative Artificial Intelligence

Positive to Have Skill

Other Requirements

📌 Technical Lead (India)
🏢 HCLTech
📍 India