What are the responsibilities and job description for the AI ML Data Architect position at Digital Minds Technologies Inc.?
Job Details
Title : AI/ ML Data Architect
Location: Charlotte NC (On-site)
Job Description:
Key Responsibilities:
Data Architecture & Strategy
Design and implement scalable, high-performance data architectures for AI/ML applications.
Develop data governance, security, and compliance frameworks for AI/ML data ecosystems.
Define and implement data integration, processing, and storage best practices.
AI/ML Data Engineering
Build end-to-end data pipelines to support AI/ML model training, inference, and monitoring.
Optimize data ingestion, transformation, and feature engineering for ML models.
Implement data versioning, lineage, and metadata management.
Cloud & Big Data Technologies
Design AI/ML data solutions on AWS, Azure, or Google Cloud Platform.
Utilize big data technologies such as Apache Spark, Hadoop, Kafka, and Delta Lake.
Implement serverless architectures, containerized workloads (Kubernetes, Docker), and MLOps frameworks.
AI Model Deployment & ML Ops
Work with ML engineers and data scientists to deploy AI/ML models at scale.
Establish MLOps best practices, including automated model retraining, monitoring, and CI/CD pipelines.
Ensure models ar efficient, explainable, and compliant with enterprise policies.
Security, Compliance & Performance Optimization
Implement data security, access controls, and compliance with GDPR, HIPAA, and other regulations.
Optimize data storage, retrieval, and compute performance for AI/ML workloads.
Ensure high availability, disaster recovery, and fault tolerance of AI data pipelines.
Collaboration and Thought Leadership
Work closely with data scientists, engineers, and business leaders to align AI/ML strategies with business goals.
Provide technical leadership and mentorship to teams working on AI/ML solutions.
Stay updated with emerging AI/ML, data engineering, and cloud technologies.
Required Skills & Qualifications:
Bachelor s or Master s degree in Computer Science, Data Engineering, AI/ML, or related field.
5 years of experience in data architecture, data engineering, or AI/ML infrastructure.
Expertise in big data technologies (Spark, Kafka, Hadoop, Delta Lake, etc).
Proficiency in cloud platforms (AWS, Azure, or Google Cloud Platform) and data services.
Strong knowledge of SQL, Python, Scala, or Java.
Hands-on experience with AI/ML model deployment, MLOps, and CI/CD.
Experience with Kubernetes, Docker, and serverless computing.
Strong understanding of data governance, security, and compliance.
Preferred Certifications:
AWS Certified Data Analytics Specialty
Google Professional Data Engineer
Microsoft Certified: Azure Data Engineer Associate
Databricks Certified Data Engineer
