Senior Solution Architect – Data, ML and AI

Role Purpose

The Senior Solution Architect – Data, ML & AI is a strategic, customer-facing role responsible for designing and delivering end-to-end data and AI solutions on AWS. This individual will focus on transforming raw data into scalable, intelligent systems using services like SageMaker, Glue, Redshift, and Lake Formation. Acting as a trusted advisor, this architect bridges business challenges with technical innovation—bringing cloud-native data architecture, machine learning, and pre-sales strategy together to create real impact.

Key Responsibilities

• Lead pre-sales engagements by understanding customer needs and proposing data and AI-driven solutions.

• Architect end-to-end data pipelines, including data lakes, warehouses, and real-time streams.

• Design and implement machine learning workflows from feature engineering to deployment and monitoring.

• Utilize AWS services such as SageMaker, Glue, Lambda, Fargate, Redshift, and Lake Formation.

• Drive data governance, security, and compliance best practices in all architectures.

• Collaborate cross-functionally with data engineers, analysts, and DevOps teams.

• Define and implement MLOps pipelines for model lifecycle management.

• Contribute to proposals, estimates, and technical presentations for customer engagements.

Proficiencies, Skills, Experience and Traits

• Minimum 5 years of relevant experience in data architecture, machine learning, or AI solution delivery.

• Proven track record of delivering ML solutions in production using Python and AWS tools.

• Deep understanding of data engineering concepts including data lakes, ETL pipelines, feature engineering, governance, and metadata management.

• Hands-on experience with AWS services such as SageMaker, Glue, Redshift, Lake Formation, Lambda, and Fargate.

• Familiarity with MLOps pipelines, model versioning, monitoring, and CI/CD best practices.

• Experience supporting pre-sales engagements, technical discovery, and customer solutioning.

• Comfortable presenting technical concepts to both technical and non-technical stakeholders.

• Awareness of Generative AI (GenAI) trends and how they apply to modern data and AI solutions.

• Preferred: Experience with AWS CDK, Terraform, Kinesis, Kafka, and feature stores.

• Holds or is pursuing AWS certifications (e.g., Machine Learning – Specialty, Solutions Architect – Professional).

• Strategic thinker, highly collaborative, and proactive in finding and solving complex challenges.

• Passionate about data-first approaches and continuous learning in the AI/ML and cloud space.