AI Platform Architect

- Define and own the HLD and LLD documentation for the AI platform on AWS and Databricks - Design scalable, secure, and cost-optimized architectures for data processing, feature engineering, model training, and inference pipelines - Establish standards, patterns, and best practices for platform components including Lakehouse architecture, MLOps, and data governance - Architect and guide implementation of Databricks workspace setup, clusters, Unity Catalog, Delta Lake, workflows, and model serving - Lead the integration of the platform with key internal systems such as Radar, EDP (Enterprise Data Platform), and other upstream/downstream applications - Define API, event, and data exchange patterns for cross-system interactions - Design cloud-native solutions leveraging AWS services such as S3, IAM, Lambda, EMR, Glue, ECS/EKS, Step Functions, CloudWatch, and networking components - Ensure platform scalability, reliability, observability, and high availability - Oversee IAM policies, security controls, and data protection mechanisms - Define architecture for end-to-end ML lifecycle, including model development, experiment tracking, CI/CD, deployment, and monitoring - Evaluate technologies and frameworks for versioning, lineage, feature stores, and ML governance - Guide teams in implementing reusable ML workflows and enterprise-grade AI components - Work closely with engineering teams, product owners, data scientists, and security/infra teams to align technical design with business goals - Provide architectural guidance during development, testing, and production rollout - Review technical deliverables and ensure adherence to architectural guidelines - Produce detailed and clear technical documentation (HLDs, LLDs, architecture diagrams, integration specs) - Participate in architecture review boards and ensure compliance with enterprise standards - Support capacity planning, cost optimization, and risk assessments