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AI Solutions Architect

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Onsite

AI Solutions Architect

HCLTech

AI Solutions Architect

Skills

MongoDBNextDockerReactMicroservicesPostgreSQLKafkaRedisRabbitMQMachine LearningAIKubernetesComplianceAzure

17 days ago

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Important information


Contract type:

Permanent

Location:

London, UK

Work mode:

Onsite

Published on:

8 October 2026

What they need


Key Responsibilities

  • Lead the architecture, design, and development of enterprise-scale web applications with integrated Generative AI capabilities.
  • Define technical standards, architectural patterns, and best practices for scalable and secure software systems.
  • Collaborate with product, AI/ML, DevOps, and security teams to ensure alignment between business goals and technical execution.
  • Oversee full-stack development lifecycle, ensuring robust design, performance tuning, and security compliance.
  • Evaluate and integrate AI components such as LLMs, embedding services, and vector databases into product architecture.

Mandatory Technical Skills

  1. Cloud-Native Architecture & Microservices
  2. Proven experience designing scalable, cloud-native applications using microservices and event-driven patterns on Azure Ecosystem.
  3. Performance Optimization

Implementing caching strategies (Redis, CDN), asynchronous job processing (RabbitMQ, Kafka), and load-balanced architectures using Kubernetes or serverless platforms.

  1. GenAI Integration & LLM Orchestration
  2. Hands-on with Generative AI technologies—Azure OpenAI, prompt engineering, RAG pipelines, embeddings, and vector search (e.g., Azure AI search, Azure PostgreSQL etc..).
  3. Backend Engineering (APIs, Data, Security)
  4. Strong expertise in REST APIs with versioning, throttling, and gateway integrations., PostgreSQL/MongoDB, OAuth2/SSO, and secure coding aligned with GDPR/SOC2 standards.
  5. Frontend Development (React/Next.js)
  6. Deep experience building enterprise-grade UIs using React.JS, component libraries, and modern design systems.
  7. DevOps & Infrastructure as Code
  8. Proficiency in Azure CI/CD pipelines , Docker, and Kubernetes for automated, scalable deployments.
  9. Real-Time & Scalable UI Patterns
  10. Experience with WebSockets/SSE, UI performance optimization, and handling large-scale dynamic frontends.

Testing, Observability & Quality Engineering

Competence in automated unit/E2E testing , frontend performance profiling, and monitoring of Deployed solutions

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