Senior Solution Architect - Data & AI Platform

Description :

We are seeking a highly skilled Senior Solutions Architect Data & AI (AWS) to join our team. This role will be instrumental in designing, architecting, and delivering scalable data platforms and AI/ML solutions on AWS for enterprise and digital-native clients.

The ideal candidate is both a hands-on technologist and a strategic advisor, able to bridge business needs with technical execution.

Key Responsibilities :

Solution Architecture & Design :

- Architect end-to-end data, analytics, and AI/ML solutions leveraging AWS services (S3, Redshift, Glue, EMR, SageMaker, Bedrock, Lambda, etc.).

- Design secure, highly available, and cost-optimized architectures aligned with AWS Well-Architected Framework.

- Translate complex business requirements into scalable cloud-native architectures.

Client Engagement :

- Act as a trusted advisor to CXOs, CTOs, and business stakeholders on their data and AI strategy.

- Conduct discovery workshops, PoCs, and solution deep-dives with clients.

- Present technical solutions clearly to both technical and non-technical audiences.

Delivery Excellence :

- Oversee the implementation of data pipelines, lakes, warehouses, and AI/ML models on AWS.

- Provide governance, performance tuning, and optimization guidance across the project lifecycle.

- Ensure best practices for data security, compliance, and responsible AI are embedded in solutions.

Thought Leadership :

- Mentor junior architects and engineers, building strong internal capability.

- Contribute to IP creation, reusable accelerators, and AWS competency achievements.

- Represent the company in AWS/AI/Cloud forums, webinars, and events.

Skills & Qualifications :

- Proven experience (8 to 10 years) in designing and implementing cloud-native data and AI solutions.

- Hands-on expertise with AWS services: Glue, Redshift, EMR, Athena, Kinesis, SageMaker, Bedrock, OpenSearch, QuickSight.

- Strong grounding in data engineering, ETL, data lakes/warehouses, and AI/ML model lifecycle.

- Proficiency in Python/Scala/SQL and familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn).