Principal Architect

Location: Bangalore, India

Experience: 15-20 years (overall), with 7+ years in cloud solution architecture/presales

Primary Tech Focus: Data Platforms and AI/ML on AWS (Azure/GCP exposure required)

We are seeking a highly experienced, customer-focused Presales Architect to join our Solution Engineering team in Mumbai. In this role, you will partner with enterprise customers to shape data and AI strategies on AWS, design modern data architectures, and support sales cycles through solutioning, workshops, proposals, and demonstrations. You will be expected to bring strong hands-on architecture depth across AWS IaaS/PaaS services and data/AI stacks, along with working knowledge of Azure and GCP to support multi-cloud and hybrid discussions.

· Engage with enterprise stakeholders to understand business objectives, technical requirements, and success criteria.

· Own end-to-end solution architecture for Data/AI initiatives on AWS, including target state architecture, security, and operating model considerations.

· Design and present modern data architectures (lakehouse, data lake, streaming, analytics) and AI/ML solutions aligned to customer use cases.

· Create high-level designs, reference architectures, sizing assumptions, and implementation roadmaps; articulate trade-offs and risks.

· Lead technical discovery, workshops, and solution deep-dives; drive proof of concepts (PoCs) and technical validations when required.

· Support RFI/RFP responses, write technical proposals, and deliver compelling presentations and demos to technical and executive audiences.

· Collaborate closely with Sales, Delivery, and Product teams to ensure proposals are feasible, commercially aligned, and delivery-ready.

· Guide customers on cloud foundations: networking, identity, governance, security controls, landing zones, and cost optimization for data/AI workloads.

· Contribute to reusable assets (solution accelerators, templates, reference designs) and mentor junior architects/engineers as needed.

· AWS core architecture: VPC, EC2, EKS/ECS/Fargate, S3, EBS/EFS, ELB/ALB/NLB, Auto Scaling, Route 53.

· Serverless and integration: Lambda, API Gateway, Step Functions, EventBridge.

· Databases and storage: RDS, Aurora, DynamoDB; data storage patterns for analytics.

· Security and governance: IAM, Organizations/Control Tower concepts, KMS, WAF, CloudTrail, GuardDuty, Security Hub; secure-by-design principles.

· DevOps/IaC: Terraform and/or CloudFormation, CI/CD tooling (e.g., Jenkins/GitHub Actions), GitOps patterns; observability fundamentals.

· Hands-on architecture experience with AWS analytics and data engineering services such as Redshift, Glue, Athena, EMR, Lake Formation, Kinesis/MSK, and S3-based lake/lakehouse patterns.

· Design of batch and streaming pipelines, data modeling, orchestration, and performance optimization for analytics workloads.

· AI/ML solutioning using SageMaker and/or other ML frameworks; understanding of MLOps concepts (model lifecycle, CI/CD for ML, monitoring).

· Data governance, metadata/catalog, access control, encryption, and privacy best practices for enterprise analytics.

· Experience translating business problems into measurable data/AI outcomes and value cases (ROI, time-to-insight, operational efficiency).

· Working knowledge of Azure and GCP services to support multi-cloud/hybrid solution discussions and customer constraints.

· Azure examples: Synapse, ADLS, Databricks, AKS, Azure ML, networking/identity basics.

· GCP examples: BigQuery, GCS, Dataproc, GKE, Vertex AI, networking/identity basics.

· Ability to compare and map comparable services across AWS, Azure, and GCP and recommend fit-for-purpose architectures.

· Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field (or equivalent experience).

· 15-20 years of overall IT experience, including significant experience in presales/solution architecture for cloud transformation programs.

· Strong presales capability: discovery, storytelling, executive communication, proposal writing, and stakeholder management.

· Proven experience designing and presenting AWS-based architectures for enterprise-scale data platforms and AI/ML workloads.

· Solid understanding of scalability, availability, resiliency, security, and cost optimization principles for cloud solutions.

· Comfortable engaging with CXOs, enterprise architects, data leaders, and engineering teams.

· AWS Certified Solutions Architect - Professional (preferred) and/or relevant data/ML certifications.

· Experience with MLOps tooling such as MLflow, Kubeflow, Airflow, or similar orchestration/monitoring platforms.

· Exposure to industry domains such as BFSI, healthcare, retail, or large-scale cloud modernization programs.