Data Scientist Advanced Analytics
Experience Required: 6 years total (2 years relevant in RAG / LLM-based systems)
Location: Open / Any Location (India)
Compensation: 13 15 LPA
Employment Type: Full-time
Role Overview:
We are looking for an experienced Data Scientist Advanced Analytics with strong expertise in Python Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. The ideal candidate will be responsible for designing building and optimizing intelligent retrieval systems that combine LLM reasoning with real-time document understanding
Key Responsibilities:
Design and implement Retrieval-Augmented Generation (RAG) pipelines and architectures.
Develop document retrieval contextual augmentation and chunking strategies for large-scale unstructured data.
Optimize RAG indexing retrieval accuracy and context relevance using advanced evaluation metrics.
Implement fine-tuning and prompt engineering techniques to improve retrieval and generation quality.
Manage token limits context windows and retrieval latency for high-performance inference.
Integrate LLM frameworks like LangChain or LlamaIndex for pipeline orchestration.
Utilize APIs from OpenAI Hugging Face Transformers or other LLM providers for model integration.
Perform noise reduction diversity sampling and retrieval optimization to enhance output reliability.
Collaborate with cross-functional teams to deploy scalable RAG-based analytics solutions.
Requirements
Programming: Strong hands-on experience with Python.
RAG Expertise: In-depth understanding of RAG pipelines RAG architecture and retrieval optimization.
Vector Databases: Practical experience with FAISS Pinecone Weaviate ChromaDB or Milvus.
Embedding Models: Knowledge of generating and fine-tuning embeddings for semantic search and document retrieval.
LLM Tools: Experience with LangChain LlamaIndex OpenAI API and Hugging Face Transformers.
Optimization: Strong understanding of token/context management retrieval latency and inference efficiency.
Evaluation Metrics: Familiarity with Retrieval Accuracy Context Relevance and Answer Faithfulness.
Good to Have:
Experience in MLOps for deploying and monitoring LLM/RAG-based solutions.
Understanding of semantic search algorithms and context ranking models.
Exposure to knowledge retrieval contextual augmentation or multi-document summarization.
Masters degree in Computer Science Artificial Intelligence Data Science or related field.
Required Skills:
Experience 3-5 years of hands-on software development Experience working in Agile teams using tools like Azure DevOps or JIRA Technical Skills Proficient in: Java Spring Boot Hibernate MySQL RESTful APIs Strong front-end development using JavaScript HTML CSS Solid understanding of Object-Oriented Design and common design patterns Skilled in SQL and database interactions
Required Education:
BE
Data Scientist Advanced AnalyticsExperience Required: 6 years total (2 years relevant in RAG / LLM-based systems) Location: Open / Any Location (India) Compensation: 13 15 LPA Employment Type: Full-timeRole Overview:We are looking for an experienced Data Scientist Advanced Analytics with strong e...
Data Scientist Advanced Analytics
Experience Required: 6 years total (2 years relevant in RAG / LLM-based systems)
Location: Open / Any Location (India)
Compensation: 13 15 LPA
Employment Type: Full-time
Role Overview:
We are looking for an experienced Data Scientist Advanced Analytics with strong expertise in Python Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. The ideal candidate will be responsible for designing building and optimizing intelligent retrieval systems that combine LLM reasoning with real-time document understanding
Key Responsibilities:
Design and implement Retrieval-Augmented Generation (RAG) pipelines and architectures.
Develop document retrieval contextual augmentation and chunking strategies for large-scale unstructured data.
Optimize RAG indexing retrieval accuracy and context relevance using advanced evaluation metrics.
Implement fine-tuning and prompt engineering techniques to improve retrieval and generation quality.
Manage token limits context windows and retrieval latency for high-performance inference.
Integrate LLM frameworks like LangChain or LlamaIndex for pipeline orchestration.
Utilize APIs from OpenAI Hugging Face Transformers or other LLM providers for model integration.
Perform noise reduction diversity sampling and retrieval optimization to enhance output reliability.
Collaborate with cross-functional teams to deploy scalable RAG-based analytics solutions.
Requirements
Programming: Strong hands-on experience with Python.
RAG Expertise: In-depth understanding of RAG pipelines RAG architecture and retrieval optimization.
Vector Databases: Practical experience with FAISS Pinecone Weaviate ChromaDB or Milvus.
Embedding Models: Knowledge of generating and fine-tuning embeddings for semantic search and document retrieval.
LLM Tools: Experience with LangChain LlamaIndex OpenAI API and Hugging Face Transformers.
Optimization: Strong understanding of token/context management retrieval latency and inference efficiency.
Evaluation Metrics: Familiarity with Retrieval Accuracy Context Relevance and Answer Faithfulness.
Good to Have:
Experience in MLOps for deploying and monitoring LLM/RAG-based solutions.
Understanding of semantic search algorithms and context ranking models.
Exposure to knowledge retrieval contextual augmentation or multi-document summarization.
Masters degree in Computer Science Artificial Intelligence Data Science or related field.
Required Skills:
Experience 3-5 years of hands-on software development Experience working in Agile teams using tools like Azure DevOps or JIRA Technical Skills Proficient in: Java Spring Boot Hibernate MySQL RESTful APIs Strong front-end development using JavaScript HTML CSS Solid understanding of Object-Oriented Design and common design patterns Skilled in SQL and database interactions
Required Education:
BE
View more
View less
Job Location:
Monthly Salary:
Experience Required:
Posted on:
14 hours ago
Vacancies:
1 Vacancy
