AI Solution Architecture

As a Data/AI Solutions Architect, you will be responsible for designing scalable, highly secure, and resilient data and AI systems, while driving strategic initiatives including Machine Learning,LLMOps, RAG, and GCP cloud migration. This role bridges business needs with technical execution, involving stakeholder management, cloud-native architecture development, and ensuring compliance with data governance, security, and ethical AI standards.

Key Responsibilities:

• Strategy & Design: Define end to end architectures for AI, ML, data, and agentic AI solutions running on Google Cloud Platform.Develop and maintain iterative designs aligned to modern engineering practice. Ensure alignment to platform strategy, technology standards, and security controls.
• Technical Leadership: Deliver high-quality, secure solutions using GCP cloud-native services, and event- driven patterns. Architect GCP workloads using services such asBigQuery, Cloud Run, GKE, Cloud Storage, Dataflow, Pub/Sub, Vertex AI, Looker, and Cloud Composer.
• Cloud, Platform & Integration: Apply strong understanding of Kubernetes, service mesh, ingress, workload identity, and network/security boundaries.
• Design Patterns: Design and implement API and service integration patterns using Istio, Apigee, and modern microservice standards. Integrate enterpriseAuthN/AuthZ patterns (OIDC, IAM roles, workload identities, Apigee security policies)
• Governance & Compliance: Ensure solutions meet strict regulatory, risk management, and security standards.
• Collaboration: Work with engineering, data science, and business teams to translate requirements into actionable, high-level designs.

Required Skills & Experience:

• Cloud Expertise: Proven experience with major Cloud platforms like GCP/Azure.
• Data & AI Knowledge: Deep understanding of modern data architecture, Machine Learning, and AI tools. Experience in largescale data migrations with ETL toolsets, DataStage decommissioning paths, or modern data movement patterns is beneficial.
• Architecture & Stakeholder Leadership: Design orchestrations and technical patterns foragentic AI , LLM based systems, RAG, and workflow orchestration. Ability to articulate trade-offs, lead design decisions, and shape technical direction.
• Communication: Strong ability to communicate complex technical concepts in the design forums and participate in architectural governance (ARC, AWG) and represent the lab’s technical direction.

Background:

Experience in the banking or financial industry is beneficial.

Beneficial Skills:

• Experience with Vertex AI or other model lifecycle tooling.
• Knowledge ofLangChain/LangGraph, RAG patterns, and AI workflow orchestration.
• Experience in multi cloud (GCP/Azure/AWS) or hybrid environments.
• Understanding of enterprise integration and event architecture.

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