AI Platform Architect

The Role

We are seeking a Data Platform Architect with an AI-first mindset to design and lead the implementation of a modern, enterprise-grade data architecture. You will be responsible for building the technical infrastructurespanning data lakes, feature stores, and real-time pipelinesthat enables our data scientists and AI engineers to move from experimentation to high-impact production environments.

Responsibilities:

• Architectural Blueprinting:
• Design scalable and secure data platform blueprints (e.g., Lakehouse, Data Mesh, or Data Fabric) that support diverse AI workloads, including generative AI and classical machine learning.
• Building scalable, cloud-native storage and processing frameworks (data lakes, lakehouses) capable of handling massive datasets for model training
• AI Data Infrastructure Design: Develop specific architectures for AI-driven workflows, including feature stores, real-time data streaming (Kafka/Spark), and automated machine learning pipelines.
• Data Lifecycle Management:
• Oversee the end-to-end data lifecycle, from high-fidelity data acquisition and cleaning to preprocessing and model serving.
• Data Pipeline Automation: Creating end-to-end automated pipelines for data ingestion, cleaning, and feature engineering to reduce the time from data raw state to ML model input.
• Architecting systems that support streaming data (e.g., Kafka, Kinesis) for low-latency inference in applications like IoT, fraud detection, and customer experience
• Implementing strict governance, including metadata management, data lineage (tracking data origin), and quality monitoring to ensure "clean" data, preventing model failure.
• Governance & Ethics: Establish unified data governance frameworks that ensure security, privacy (GDPR/CCPA), and compliance while mitigating algorithmic bias.
• Stakeholder Collaboration: Act as the technical bridge between business leadership, data science teams, and IT infrastructure to align technology with strategic AI objectives.
• Security & Compliance: Embedding zero-trust principles, role-based access control (RBAC), and regulatory compliance (GDPR, HIPAA) directly into the data architecture.
• MLOps Collaboration: Working closely with data scientists and MLOps teams to integrate feature stores, model registries, and monitoring tools for continuous retraining

Qualifications & Experience

• Bachelors or Masters degree in Computer Science, Information Systems, Engineering, or a related field.
• 1016 years of experience in data warehouse /Bigdata Data platform skills, with at least 3-5 years focused on AI/ML supporting infrastructure.
• Deep expertise in cloud platforms like AWS, Azure, or Google Cloud, and big data technologies such as Apache Spark, ADF, Databricks, and Snowflake.
• Experience with data governance, security, and compliance standards.
• Excellent communication and stakeholder management skills.