Technical Presales

Job Description Solution Architect - Presales (Intelligent Application Development & Data/AI)

Location: Delhi NCR | Experience: 1520 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.

1520 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.