Title:  Architect

Area(s) of responsibility

Job Description: GenAI Solution Architect

Key Responsibilities

1. Architecture Ownership

• Define and own end-to-end solution architecture for GenAI applications across cloud (AWS/Azure) environments.
• Design scalable architectures integrating:
• LLM orchestration (e.g., router/config layers)
• APIs (Frontend, Core, Backend)
• Vector databases (e.g., PG Vector)
• Kubernetes-based deployments
• Cloud-native services (S3, Aurora/PostgreSQL, Secret Manager)
• Ensure alignment with enterprise security, including SSO (Entra ID), network restrictions, and secure access patterns.

2. Design & Enhancements

• Lead design of new features and enhancements in GenAI platforms.
• Evaluate and incorporate:
• LLM platforms (e.g., Bedrock/OpenAI/Azure OpenAI)
• Observability tools (Grafana, Prometheus, Loki)
• DevSecOps pipelines (Jenkins, Bitbucket, Artifactory)
• Continuously optimize architecture for performance, scalability, cost, and reliability.

3. Client Engagement & Approvals

• Work directly with customers to:
• Understand business requirements and translate them into technical solutions
• Present architecture designs and solution approaches
• Obtain architecture approvals and align on roadmap
• Act as the primary technical interface for all architecture-related discussions.

4. Engineering Leadership

• Guide development teams on:
• Implementation aligned with approved architecture
• API design and microservices patterns
• Secure coding and deployment practices
• Conduct architecture and code reviews to ensure adherence to standards.
• Provide technical mentorship and resolve complex issues.

5. Delivery Governance

• Ensure all development adheres to:
• Approved architecture
• Customer requirements
• Security and compliance standards
• Proactively identify and resolve architectural risks and bottlenecks.
• Track alignment with DevOps/ML Ops practices and CI/CD pipelines.

6. Deployment & Operations Alignment

• Define deployment strategies using:
• Kubernetes clusters with ingress/load balancing
• CI/CD pipelines for automated deployments
• Ensure integration with monitoring/logging frameworks (Grafana, Prometheus, AWS Monitoring).
• Drive best practices for production readiness and operational excellence.