Generative AI Architect - Machine Learning

Gen AI ArchitectJob Description :

• 10+ years in ML/Engineering.

• At least 5 years in architecture roles.

• Proven experience delivering production-grade solutions on Azure.

• Hands-on ownership of end-to-end application lifecyclefrom design to deployment.

• Proven track record of communicating complex technical concepts to both developer teams and executive stakeholders.

Responsibilities :

• Lead architecture and development of enterprise web applications with integrated Generative AI.

• Serve as a primary technical partner to clients and developers, guiding them from early experimentation through production scale deployments.

• Roll up your sleeves on POCs, prototypes, and pilots, and help clients operationalize them into production-grade systems.

• Design and validate end-to-end AI and GenAI solutions architectures spanning the full stack, from foundation model integration to agentic workflows, orchestration, evaluation, and observability.

• Define scalable, secure architectural patterns and implementation standards.

• Work closely with product, AI/ML, DevOps, security, and network teams to align business and technical goals.

• Drive full-stack development best practices across backend, frontend, and infrastructure.

• Architect and integrate LLMs, embeddings, RAG pipelines, and vector databases into production systems.

• Ensure production readinesssecurity, networking, monitoring, performance, and compliance.

Mandatory Technical Skills : Cloud Architecture (Azure) :

• Deep experience designing cloud-native, microservices and event-driven architectures. Expertise with Azure App Services, AKS, Functions, API Management, Event Grid, Service Bus, Storage, and Key Vault.

• Strong understanding of subscription design, resource hierarchy, and environment isolation.

Networking & Security :

• Hands-on experience with :

• VNETs, subnets, private endpoints, service endpoints.

• NSGs, ASGs, routing, firewalls, and VPN/ExpressRoute connectivity.

• Whitelisting, IP restrictions, certificates, TLS, and enterprise-grade authentication flows.

• Experience implementing Zero Trust, RBAC, managed identities, and secure secret handling.

Performance & Scalability :

• Expertise in caching (Redis/CDN), async processing (RabbitMQ/Kafka), load balancing, auto-scaling, and performance tuning.

GenAI Integration :