AI/ML Data Architect

We are seeking a highly skilled AI/ML Data Architect to design and implement scalable data architectures that support AI and machine learning workloads. The ideal candidate will have expertise in big data technologies, cloud platforms, data pipelines, and AI/ML model deployments. You will work closely with data scientists, engineers, and business stakeholders to create a robust data ecosystem that enables advanced analytics and AI-driven decision-making. 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 GCP. • 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 are 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 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 GCP) 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. Certifications • Google Professional Data Engineer

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