Principal Architect

Job Description – Solution Architect (Intelligent Application Development & Data/AI) Location: Delhi NCR | Experience: 15–20 years | Domain: Cloud (Azure/GCP/AWS), Data/AI, Intelligent App Development Job Summary We are looking for a highly experienced Solution Architect to lead the design and delivery of modern, cloud-native solutions across Intelligent Application Development (IAD) and Data/AI programs. The role requires deep hands-on architecture expertise on Microsoft Azure and Google Cloud Platform (GCP), with strong exposure to AWS in multi-cloud or hybrid environments. You will collaborate with business stakeholders, engineering teams, and delivery leaders to define target architectures, guide implementation, ensure non-functional requirements, and drive measurable outcomes. Key Responsibilities Solution Architecture & Delivery Leadership · Own end-to-end solution architecture for large-scale transformation programs spanning application modernization and data/AI platforms. · Translate business objectives into target-state architectures, solution blueprints, and implementation roadmaps (phased, value-driven). · Define integration patterns and reference architectures for microservices, APIs, event-driven systems, and domain-aligned platforms. · Lead architecture governance: standards, design reviews, architecture decisions (ADRs), and alignment to enterprise principles. · Guide engineering teams through build, test, deployment, and operational readiness; unblock teams and manage technical risks. · Ensure solutions meet NFRs: scalability, performance, resiliency, security, compliance, and cost efficiency (FinOps). · Partner with program/product leadership to plan milestones, dependencies, and cross-team integration. Intelligent Application Development (IAD) · Architect cloud-native applications using modern patterns: microservices, serverless, containers, and event streaming. · Design API-first platforms (REST/GraphQL), integration using API gateways/service mesh, and secure identity-driven access. · Define DevSecOps automation: CI/CD, IaC, policy-as-code, testing strategy, observability, and SRE practices. · Drive modernization initiatives: monolith to microservices, containerization, refactoring, re-platforming, and legacy integration. · Evaluate and recommend GenAI-enabled application capabilities (assistants, copilots, RAG patterns) with responsible AI controls. Data, AI/ML & Analytics · Design modern data architectures: lakehouse/data warehouse, streaming analytics, and governed data products. · Architect data ingestion, transformation, and orchestration pipelines with reliability, lineage, and quality controls. · Lead AI/ML solutioning: model development lifecycle, MLOps, feature stores, model deployment, monitoring, and drift management. · Enable GenAI workloads: vector search, embeddings, prompt management, RAG pipelines, and evaluation frameworks. · Define data governance and security: IAM/RBAC, encryption, key management, cataloging, DLP, privacy, and regulatory compliance. Cloud Platforms & Services (Hands-on Architecture) Microsoft Azure (Primary) · Compute & Containers: AKS, App Service, Functions, Container Apps. · Data & Analytics: Azure Synapse / Microsoft Fabric, Azure Databricks, ADLS Gen2, Data Factory, Event Hubs, Stream Analytics. · AI/ML: Azure Machine Learning, Cognitive Services / Azure AI services, model endpoints, prompt flow (where applicable). · Security & Governance: Entra ID (Azure AD), Key Vault, Defender for Cloud, Policy, Private Link, Landing Zones. Google Cloud Platform (Required) · Compute & Containers: GKE, Cloud Run, Cloud Functions. · Data & Analytics: BigQuery, Dataproc, Dataflow, Cloud Storage, Pub/Sub, Looker. · AI/ML: Vertex AI (training, pipelines, model registry, endpoints), embeddings/vector search patterns. · Security & Governance: IAM, KMS, VPC Service Controls, Organization policies. AWS (Multi-cloud / Hybrid) · Core services and architecture: VPC, EC2, S3, IAM, EKS/ECS, Lambda, API Gateway. · Data/AI exposure: Redshift/Athena/Glue/EMR, SageMaker (or equivalent patterns). · Multi-cloud networking and identity considerations (connectivity, IAM federation, governance). Required Skills & Qualifications · Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field. · 15–20 years of overall IT experience, with significant experience as a Solution Architect / Technical Architect on complex programs. · Strong architecture depth in Azure and GCP for application modernization and data/AI solutions; AWS exposure in multi-cloud environments. · Proven expertise in cloud-native design, microservices, containers, serverless, API management, and event-driven architectures. · Strong data platform experience (lakehouse/warehouse, streaming, orchestration, governance) and AI/ML lifecycle understanding. · DevSecOps and IaC experience: Terraform/Bicep/ARM, CI/CD (Azure DevOps, GitHub Actions, Jenkins), observability (Azure Monitor, Cloud Logging, Prometheus/Grafana). · Excellent stakeholder management, communication, and documentation skills; ability to present to enterprise architects and senior leadership. Certifications (Preferred) · Microsoft Certified: Azure Solutions Architect Expert. · Google Professional Cloud Architect and/or Professional Data Engineer. · Relevant AI/ML certifications (Azure AI Engineer, Vertex AI, Databricks, etc.). · AWS Solutions Architect (Associate/Professional) – desirable. Nice to Have · Experience with enterprise architecture frameworks, reusable reference architectures, and architecture governance boards. · Hands-on experience implementing GenAI solutions with responsible AI, security, and evaluation best practices. · Domain experience in BFSI, retail, manufacturing, healthcare, or public sector transformation programs. · Experience with modern integration and messaging platforms (Kafka, Pub/Sub, Event Hubs) and API observability/management. Soft Skills · Strong consultative mindset with ability to simplify complex technical choices into business outcomes. · Structured problem-solving and ability to mentor architects/engineers across teams. · Ownership, bias for action, and ability to handle ambiguity in large transformation environments.