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
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
Other Requirements
#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply-#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply-
