Data and MLOps Engineer

5 days ago

Manila, National Capital Region, Philippines GenuineXs Full time ₱1,200,000 - ₱2,400,000 per year

Job Summary:

We are seeking a highly skilled Data and MLOps Engineer with deep technical expertise across cloud ecosystems, particularly within Azure. The ideal candidate has a strong foundation in MLOps, data pipeline architecture, and healthcare data standards. This role will be crucial in designing, implementing, and scaling machine learning infrastructure and data workflows across various environments. 

Key Responsibilities:

Data Engineering & Pipeline Development

  • Design, develop, and maintain scalable data pipelines for processing large volumes of structured and unstructured data.

  • Implement best practices for data storage, retrieval, and access control to ensure data integrity, security, and compliance.

  • Proactively identify and resolve data quality issues and pipeline disruptions.

Data Management & Preprocessing

  • Preprocess and clean large datasets using Python, Azure tools, and other data manipulation technologies.

  • Work within Snowflake and Azure Storage (Blob, Postgres) for data analytics and management.

Search & Retrieval Systems

  • Apply in-depth knowledge of search algorithms, indexing techniques, and retrieval models.

  • Leverage tools like Azure AI Search and vector databases (e.g., Pinecone) to build effective information retrieval solutions.

  • Use chunking techniques and integrate vector-based search models.

MLOps & Model Deployment

  • Automate ML workflows via CI/CD pipelines for streamlined training, testing, validation, and deployment.

  • Deploy, monitor, and scale machine learning models using platforms such as Azure, Snowflake, and AWS.

  • Track model versions and performance using tools like MLflow, DVC, and Azure ML.

Healthcare Data Compliance

  • Ensure HIPAA and FHIR standards are met when working with PII/PHI data.

  • Apply data masking and governance practices for auditability and regulatory compliance.

Post-Deployment Monitoring

  • Monitor and log AI/ML systems post-deployment to ensure reliability and model integrity.

  • Track model drift and implement performance optimizations to maintain accuracy.

Collaboration & Communication

  • Collaborate with cross-functional teams including data scientists and product teams.

  • Translate complex technical concepts to non-technical stakeholders effectively.

Required Skills & Experience:

  • Deep hands-on experience with the Azure Cloud ecosystem (AI Search, Postgres, Blob Storage, Function App).

  • Proficiency in Python for data manipulation and automation.

  • Strong experience handling unstructured data and healthcare-related datasets.

  • Deep understanding of vectors and vector databases like Pinecone.

  • Experience scaling POCs to production environments.

  • Familiarity with Document Intelligence, Snowflake, and model governance best practices.

  • Hands-on expertise with containerization tools like Docker and Kubernetes.

  • Proven knowledge of model monitoring, versioning, and explainability tools.

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