AI Architect Enterprise AI Platform Architecture & Regulated Systems

About the Role

We are looking for a Senior AI Architect to own the end-to-end technical architecture of a production-grade, multi-agent AI platform deployed within US-regulated financial institutions.

This is a hands-on architecture role, not an advisory or strategy position. The architect will be responsible for critical design decisions across AI/ML platforms, cloud architecture, security, compliance, scalability, deployment, and operational reliability.

The role involves working with Azure and GCP, Kubernetes, BYOC deployments, confidential computing, agentic AI, RAG, LLMOps/MLOps, identity and access management, encryption, and immutable audit architecture.

Key Responsibilities

1. Platform Architecture

• Own the overall architecture of a multi-agent AI platform across Azure and GCP.
• Define component boundaries, data flows, integration patterns, and deployment topologies.
• Architect the Model Abstraction Layer for multi-model and multi-cloud flexibility.
• Design Blue-Green deployment strategies, traffic shifting, rollback, and regression gates.
• Design scalable AKS/GKE Kubernetes architectures including namespace isolation, node pools, workload identity, and resource management.

2. Security Architecture

• Architect BYOC / tenant-isolated deployments ensuring customer data remains within the client's cloud environment.
• Design offline cryptographic licensing using RSA/JWT-based mechanisms.
• Define encryption architecture using Azure Key Vault / GCP Cloud KMS, CMEK and TLS 1.3.
• Design confidential-computing architecture for secure data processing.
• Architect private networking using Azure Private Link/Endpoints and GCP VPC Service Controls/Private Service Connect.
• Design Entra ID / Google Cloud Identity, SSO, Workload Identity Federation and RBAC.
• Define immutable, tamper-evident audit logging covering agent actions, HITL decisions, evidence retrieval, and output versions.

3. AI System Architecture

• Architect multi-agent / agentic AI pipelines, workflow orchestration, agent handoffs, HITL gates, and failure handling.
• Design RAG architectures, including embeddings, vector databases, chunking, indexing, retrieval optimization, and context management.
• Design deterministic tool architectures separating AI reasoning from data retrieval.
• Ensure tool access, evidence validation, errors, and data operations are fully auditable.
• Architect stateless and reproducible agent execution.

4. Cloud & Infrastructure Architecture

• Design multi-cloud architectures across Microsoft Azure and Google Cloud Platform (GCP).
• Architect persistence using Azure SQL / PostgreSQL, Azure Blob Storage / GCS.
• Design cloud-native observability using Azure Monitor, Application Insights, Cloud Monitoring and Cloud Logging.
• Define secure container supply chains using Sigstore, Binary Authorization, Azure Container Registry and Google Artifact Registry.
• Establish Kubernetes admission and image-signing controls.

5. Enterprise Integration Architecture

• Design integrations with ERP, IAM, GRC and workflow platforms across on-premise and cloud environments.
• Architect GRC integrations using APIs and file-based interfaces.
• Define data mappings and output formats including JSON, PDF and CSV.
• Design deployment packages using Helm, Terraform and signed container images.
• Establish secure, zero-Anaptyss-access deployment processes.

Required Experience & Skills

Architecture & Cloud

• 8+ years of enterprise software architecture experience.
• At least 3 years of AI/ML platform architecture experience.
• Experience designing production systems in regulated environments, preferably financial services.
• Strong cloud-native architecture experience with Azure and/or GCP.
• Strong knowledge of Kubernetes, IAM, encryption, networking and managed cloud services.
• Experience with BYOC / tenant-isolated deployment models.
• Architecture-level experience with confidential computing such as Azure Confidential Computing or GCP Confidential VMs.

AI / ML Architecture

• Strong understanding of LLM inference architecture, model serving, API integration, latency optimization, token economics, and production failure handling.
• Hands-on experience architecting agentic AI / multi-agent systems.
• Experience with RAG architecture, embeddings and vector databases such as:
• pgvector
• Pinecone
• Weaviate
• Vertex AI Vector Search
• Equivalent technologies
• Strong understanding of LLMOps / MLOps, model versioning, Blue-Green deployments, regression testing, monitoring, and output-quality controls.
• Understanding of LLM security risks including prompt injection, data exfiltration, and context manipulation.

Security & Compliance

• Strong knowledge of RSA, JWT, asymmetric cryptography and key management.
• Experience with cloud security architecture covering:
• CMEK
• TLS
• Secrets management
• Network security
• Confidential computing
• Strong knowledge of SAML/OIDC, SSO, Workload Identity Federation and RBAC.
• Experience designing immutable audit logs and tamper-evident audit trails.
• Familiarity with US banking technology-risk frameworks such as OCC, Federal Reserve SR Letters and FFIEC guidance.

Engineering & Documentation

• Ability to create detailed architecture artefacts including:
• Component architecture diagrams
• Data-flow specifications
• Interface contracts
• Security boundaries
• Deployment topologies
• Experience preparing technical documentation for enterprise architecture, information security, model risk and regulatory review.
• Proficiency in Python and/or TypeScript sufficient to review code and prototype architectural concepts.

Good to Have

• Experience working directly with bank technology risk, information security or enterprise architecture teams.
• Multi-cloud architecture experience across both Azure and GCP.
• Confidential computing implementation experience including attestation, enclave programming or sealed storage.
• Experience designing software licensing and IP protection systems for customer-hosted deployments.
• Experience with SOC 2 Type II audit preparation.

Why Join Us?

• Work on a technically complex enterprise AI platform for regulated financial institutions.
• Solve challenges across agentic AI, LLM architecture, RAG, multi-cloud, confidential computing and cloud security.
• Work closely with senior enterprise stakeholders including bank CDOs, Chief AI Officers and Enterprise Architects.
• Opportunity to influence architecture decisions across a production-grade AI platform.