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.
