Data MLOps Engineer

TechDigital Group; Providence (RI); On-site; USD 100,000 - 160,000; Full time

Job summary

An established industry player is seeking a Data MLOps Engineer with extensive experience in the Azure ecosystem. This role involves designing scalable data pipelines, ensuring data integrity, and implementing efficient workflows for large language models. The ideal candidate will have a deep understanding of data engineering principles, particularly in handling sensitive healthcare data, and will be skilled in using tools like Snowflake and Azure AI Search. Join a dynamic team where your expertise will drive innovative solutions and improve data processing capabilities, making a significant impact in the field of data science and MLOps.

Qualifications

• 10+ years of experience in data engineering with a strong focus on Azure ecosystem.
• Hands-on experience with Python for data manipulation and MLOps.

Responsibilities

• Design and maintain scalable data pipelines for structured and unstructured data.
• Implement best practices for data storage, retrieval, and compliance.

Skills

Data Engineering

Azure Cloud

Python

Data Manipulation

MLOps

Data Quality Management

Search Algorithms

Data Processing Workflows

Problem Solving

Education

Bachelor's Degree in Data Science or related field

Tools

Azure AI Search

Snowflake

Elasticsearch

Python Libraries for Data Manipulation

Job 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.
• 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. HIPAA and 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.
• Ability 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 knowledge of 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.

Must Have:

• Minimum of 10 years' experience as a data engineer
• Hands-on experience with Azure Cloud ecosystem.
• Hands-on experience using Python for data manipulation.
• Deep understanding of vectors and vector databases.
• Hands-on experience scaling POC to production.
• Hands-on experience using tools such as Document Intelligence, Snowflake, function app, Azure AI Search.
• Experience working with PII/PHI.
• Hands-on experience working with unstructured data.