Job Summary
We are seeking a highly experienced Senior Data & ML Platform Architect to lead the design and implementation of a production-grade Data & ML platform on AWS. This hands-on role will serve as the senior technical leader responsible for architecting scalable, privacy-first data and machine learning solutions that support real-time analytics, model deployment, and platform modernization. The ideal candidate will have deep expertise in AWS, MLOps, large-scale data engineering, and platform architecture, along with the ability to collaborate effectively with senior technical stakeholders.
Key Responsibilities
• Own the end-to-end architecture for the Data & ML platform, including data ingestion, lakehouse architecture, feature pipelines, model pipelines, model serving, governance, and observability.
• Design scalable, cost-effective, and operationally efficient AWS-based data and ML solutions.
• Build and optimize MLOps capabilities, including reproducible training pipelines, model registry, automated evaluation, deployment, monitoring, drift detection, and retraining.
• Develop automated and reusable data pipeline frameworks supporting batch and streaming workloads with built-in quality, lineage, and CI/CD capabilities.
• Design data platforms that support federated data environments while addressing data residency and privacy requirements.
• Collaborate with senior technical stakeholders to define architecture, evaluate technical trade-offs, and recommend platform solutions.
• Provide technical leadership on topics including federated learning, edge inference, latency optimization, and privacy-preserving architectures.
• Implement governance, security, monitoring, and observability best practices across the platform.
• Build scalable solutions that support long-term knowledge transfer and operational ownership by internal teams.
• Mentor engineering teams and establish best practices for data engineering, MLOps, and cloud architecture.
Required Qualifications
• Strong production experience designing and implementing AWS-based Data & ML platforms.
• Hands-on expertise with AWS services including S3, Glue, SageMaker, Kinesis and/or MSK, and related cloud services.
• Proven experience implementing production-grade MLOps, including training pipelines, model registry, deployment, monitoring, drift detection, and automated retraining.
• Strong experience building large-scale batch and streaming data pipelines.
• Expertise with Lakehouse and Medallion architecture patterns.
• Experience implementing data quality frameworks, metadata management, and data lineage solutions.
• Strong understanding of CI/CD practices for data engineering and machine learning platforms.
• Experience designing scalable, reliable, and cost-optimized enterprise architectures.
• Strong knowledge of cloud architecture, data engineering, and machine learning platform design.
• Excellent analytical, problem-solving, and technical communication skills.
• Experience working directly with senior technical and business stakeholders.
Preferred Qualifications
• Experience with federated learning, privacy-preserving machine learning, secure aggregation, edge inference, or privacy-first data architectures.
• Experience in telecommunications, digital identity, fraud detection, or risk management domains.
• Experience with Apache Iceberg or other open lakehouse table formats.
• Experience with Infrastructure as Code (IaC).
• Exposure to multi-cloud environments, including GCP and hybrid/on-premises platforms.
• Previous hands-on experience as a Data Engineer or Data Scientist.
• Experience building feature stores and scalable ML platform capabilities.
