Experience: 5 to 8 Years as ML Engineer or Data Engineer
Location: Chennai
About the Role:
You will be responsible for setting up and maintaining the RAG backend infrastructure ensuring data security and compliance and integrating multiple data sources into a unified knowledge system. Over time you will transition into broader engineering tasks supporting the work of the engineering functions.
Key Responsibilities:
Design implement and maintain scalable data ingestion pipelines for unstructured and structured content.
Build and manage vector database infrastructure (e.g. Pinecone Milvus) for document indexing and retrieval.
Develop and enforce fine-grained access control mechanisms to ensure secure and compliant knowledge access.
Collaborate with domain experts to source curate and structure knowledge for RAG workflows.
Integrate document chunking embedding generation and metadata tagging into the pipeline.
Monitor and optimise retrieval performance latency and quality across the RAG stack.
Implement observability logging and evaluation metrics for pipeline health and retrieval quality.
Work with IT teams to deploy and maintain services in a self-hosted environment.
Job requirements:
Bachelors or Masters degree in computer science Data Engineering or a related field.
5-8 years of experience in ML Engineering Data Engineering or MLOps.
Proficiency in Python and experience with data manipulation analysis and visualisation libraries (e.g. Pandas NumPy Matplotlib Seaborn etc.)
Hands-on experience with vector databases and embedding techniques data pipelines
Job Title: Machine Learning Engineer Qualification: B.E/ M.E/ Experience: 5 to 8 Years as ML Engineer or Data Engineer Location: Chennai About the Role: You will be responsible for setting up and maintaining the RAG backend infrastructure ensuring data security and compliance and integrating mu...
Job Title: Machine Learning Engineer
Qualification: B.E/ M.E/
Experience: 5 to 8 Years as ML Engineer or Data Engineer
Location: Chennai
About the Role:
You will be responsible for setting up and maintaining the RAG backend infrastructure ensuring data security and compliance and integrating multiple data sources into a unified knowledge system. Over time you will transition into broader engineering tasks supporting the work of the engineering functions.
Key Responsibilities:
Design implement and maintain scalable data ingestion pipelines for unstructured and structured content.
Build and manage vector database infrastructure (e.g. Pinecone Milvus) for document indexing and retrieval.
Develop and enforce fine-grained access control mechanisms to ensure secure and compliant knowledge access.
Collaborate with domain experts to source curate and structure knowledge for RAG workflows.
Integrate document chunking embedding generation and metadata tagging into the pipeline.
Monitor and optimise retrieval performance latency and quality across the RAG stack.
Implement observability logging and evaluation metrics for pipeline health and retrieval quality.
Work with IT teams to deploy and maintain services in a self-hosted environment.
Job requirements:
Bachelors or Masters degree in computer science Data Engineering or a related field.
5-8 years of experience in ML Engineering Data Engineering or MLOps.
Proficiency in Python and experience with data manipulation analysis and visualisation libraries (e.g. Pandas NumPy Matplotlib Seaborn etc.)
Hands-on experience with vector databases and embedding techniques data pipelines