Data MLOps Engineer

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Company: Omni Inclusive

Location: Providence, RI 02902

Description:

Data MLOps Engineer
  • Experience working within the Azure ecosystem, including Azure AI Search, Azure Storage Blob, Azure Postgres and understanding how to leverage them for data processing, storage, and analytics tasks.
  • Ability to preprocess and clean large datasets efficiently using Azure Tools /Python and other data manipulation tools. Experience with techniques such as data normalization, feature engineering, and data augmentation is preferred.
  • To have background in Data Science/MLOps and proficiency in DevOps, CI/CD, Azure Cloud computing and Model monitoring.
  • Expertise in working with healthcare data standards (ex. HIP nd FHIR), sensitive data and data masking techniques to mask personally identifiable information (PII) and protected health information (PHI) is essential.
  • In-depth knowledge of search algorithms, indexing techniques, and retrieval models for effective information retrieval tasks. Familiarity with search platforms like Elasticsearch or Azure AI Search is a must.
  • Familiarity with chunking techniques and working with vectors and vector databases like Pinecone.
  • Ability to design, develop, and maintain scalable data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
  • Experience with implementing best practices for data storage, retrieval, and access control to ensure data integrity, security, and compliance with regulatory requirements.
  • Be able to implement efficient data processing workflows to support the training and evaluation of solutions using large language models, ensuring reliability, scalability, and performance.
  • Ability to proactively identify and address issues related to data quality, pipeline failures, or resource contention, ensuring minimal disruption to systems.
  • Experience with large language model frameworks, such as Langchain and know how to integrate them into data pipelines for natural language processing tasks.
  • Experience working within the snowflake ecosystem.
  • Knowledge of cloud computing principles and experience in deploying, scaling, and monitoring AI solutions on cloud platforms like Snowflake, Azure, AWS.
  • Ability to communicate complex technical concepts effectively to technical and non-technical stakeholders and collaborate with cross-functional teams.
  • Analytical mindset with a keen attention to detail, coupled with the ability to solve complex problems efficiently.
  • Knowledge of cloud cost management principles and best practices to optimize cloud resource usage and minimize costs.

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