Data ML Ops Engineer with Azure cloud

Job Location- PAN INDIA

Experience- 9+ years

Skill

Total Experience as Data Engineer.

Total Experience as ML Ops Engineer

Total Experience in Python for Data manipulation

Total Experience in using vectors and vector databases

Total experience in scaling POC to production and developing ML and AI pipelines for production

Total experience in Azure tools (Azure Document Intelligence, Azure function app, and Azure AI Search)

Total experience in Snowflake

Total experience in working with unstructured data

Total experience in working with PII/PHI

Azure Cloud and Snowflake

Azure Cloud - Experience in Azure Ecosystem (including Azure AI Search, Azure Storage Blob, Azure Postgres, and Azure SQL) with expertise in leveraging these tools for data processing, storage, and analytics tasks

Snowflake - Experience within the Snowflake ecosystem (including Snowflake data warehouse, Snowpark, Snowflake Notebooks, and Streamlit) with expertise in leveraging these tools to create data and AI pipelines

Data Scient and AI

Proficiency in data preprocessing and cleaning large datasets efficiently using Azure Tools, Python, and other data manipulation tools

Understanding of model explainability and the use of tools and techniques to provide transparent insights into model behavior

In-depth knowledge of search algorithms, indexing techniques, and retrieval models for effective information retrieval tasks

Experience with chunking techniques and working with vectors and vector databases like Pinecone

Experience with large language model frameworks, such as Langchain, and the ability to integrate them into data pipelines for natural language processing tasks

Data Engineering

Ability to design, develop, and maintain scalable data pipelines for processing and transforming large volumes of structured and unstructured data, ensuring performance and scalability.

Implement best practices for data storage, retrieval, and access control to maintain data integrity, security, and compliance with regulatory requirements

Implement efficient data processing workflows to support the training and evaluation of solutions using large language models (LLMs), ensuring that models are reliable, scalable, and performant

MLOPs/DevOps for AI

Strong background in Data Science/MLOps, with hands-on experience in DevOps, CI/CD, Azure Cloud computing, and AI model monitoring.

Experience with ML model deployment, including testing, validation, and integration of machine learning models into production systems

Knowledge of model versioning and management tools, such as MLflow or Azure Machine Learning, for tracking experiments, versions, and deployments

Model monitoring and performance optimization, including tracking model drift and addressing performance issues to ensure models remain accurate and reliable