Data & AI Solution Architect - AWS Cloud Platforms

Key Responsibilities

• AWS Data Platform Architecture
• Architect end-to-end AWS data platforms including data lakes, lakehouse architectures, streaming platforms, and analytics layers.
• Design solutions using AWS services such as Amazon S3, Glue, Athena, Redshift, EMR/Spark, Kinesis, Lambda, and managed databases.
• Define data ingestion, transformation, and orchestration patterns (batch, streaming, real-time) for large-scale enterprise workloads.
• Establish data governance, security, and access-control frameworks aligned with enterprise and regulatory standards.
• AI, ML & GenAI Architecture
• Design and guide implementation of AI/ML solutions on AWS, including model development, training, deployment, and lifecycle management.
• Architect GenAI and agentic AI solutions leveraging AWS services such as Amazon Bedrock, SageMaker, vector databases, and AI orchestration platforms.
• Define MLOps and LLMOps patterns for scalable, reliable, and cost-efficient AI operations.
• Ensure Responsible AI practices including security, privacy, explainability, and governance.
• Solution Shaping & Client Engagement
• Partner with Sales and Solution Architects to shape data and AI solution strategies for proposals and large AWS pursuits.
• Translate business requirements into scalable AWS data and AI architectures with clear value articulation.
• Engage with client stakeholders (Data Leaders, Analytics Heads, AI Leads) to act as a trusted technical advisor.
• Delivery & Architecture Governance
• Provide architectural oversight during delivery to ensure adherence to approved designs and best practices.
• Mentor data engineers, ML engineers, and platform teams on AWS data and AI patterns.
• Support performance tuning, cost optimization (FinOps), and operational readiness of AWS data and AI platforms.
• Architecture Governance
• Align architectures to AWS Well-Architected Framework and AWS data & AI best practices.
• Collaborate with AWS teams on joint innovation initiatives, accelerators, and PoCs.
• Contribute to reusable assets, reference architectures, and industry-specific data & AI offerings for AWS.

Required Experience & Qualifications

• 10–15+ years of experience in data architecture, analytics, or AI/ML engineering, with strong focus on AWS.
• Proven experience designing and delivering enterprise data lakes, analytics platforms, and AI solutions on AWS.
• Strong hands-on knowledge of AWS data services, AI/ML services, and distributed data processing frameworks.
• Experience with cloud data migration, modernization, and large-scale platform implementation.
• Ability to translate complex technical architectures into business outcomes.

Preferred Attributes

• Experience with GenAI, LLMs, vector search, and agentic AI patterns on AWS.
• Industry experience in Life Sciences, Healthcare, Manufacturing, Financial Services, or Transportation.
• Familiarity with Databricks on AWS and third-party data ecosystems.
• AWS certifications such as Data Analytics, Machine Learning, or Solutions Architect.