Description

• Strong experience designing enterprise-scale AI reference architectures across experience, agentic core, retrieval, orchestration, integration, models, data platform, and infrastructure layers.

• Deep understanding of predictive AI, generative AI, foundation models, agentic AI, RAG, vector andhybrid search, semantic layers, ontology management, knowledge graphs, enterprise search, andgoverned knowledge management.

• Ability to design agentic and application architectures including agent runtimes, reasoning loops,

tool and function calling, multi-agent orchestration, workflow routing, memory, state management,and human-in-the-loop controls.

• Experience defining AI guardrails and responsible AI controls including content filtering, prompt-

injection defense, PII detection and redaction, policy enforcement, safety classifiers, red-teaming,

evaluation gates, audit logging, and traceability.

• Strong knowledge of integration and connectivity patterns including API gateways, service mesh,

event and message streaming, connector catalogs, secure identitypropagation, schema validation,

traffic governance, quotas, and protocol mediation such as MCP and A2A.

• Understandingof model serving and lifecycle capabilities including model catalogs and registries,

commercial and open-source model hosting, model routing and abstraction, inference

infrastructure, fine-tuning, prompt libraries, benchmarking, and continuous evaluation.

• Experience with MLOps, LLMOps, and AgentOps practicesincluding pipeline orchestration, model

and prompt registries, CI/CD for models and agents, deployment and rollback, monitoring, drift

detection, qualitymonitoring, and cost and usage metering.

• Strong Azure architecture skills across Azure landing zones, subscriptions, networking, private

connectivity, identity and access management, policy, monitoring, cost management, container

platforms, AI services, data services, and secure cloud deployment patterns.

• Strong on-premises architecture skills acrossdata center hosting, virtualized infrastructure,

container platforms, GPU and accelerator capacity, storage,network segmentation, secrets

management, secure connectivity, backup, resilience, patching, and operational controls.

• Ability to design hybrid AI deployment patterns spanning Azure and on-premises environments,

including workloadplacement, data residency, latency, secure interconnect, identity federation,

private endpoints, key management, monitoring, and failover considerations.

• Strong understanding of enterprise data platform capabilities including governeddata products,

feature and embedding stores, lineage, data quality controls, classification, retention, row- and

column-level security, dynamic masking, and encryption.

• Strong understanding of infrastructure and platform capabilities including GPU and accelerator

capacity, Kubernetes platforms, autoscaling inference, isolated networks, secrets management,

storage, networking, hardened images, vulnerability scanning, observability, and capacity

management.

Experience

• 9–15+ years of overall technology experience.

• 5+ years in Solution Architecture, Enterprise Architecture, Data Architecture, Platform Architecture, or AI Architecture roles.

• Experience delivering enterprise-scale AI platforms, AI-enabled transformation initiatives, or

complex AI solution architectures.

• Experience working within regulated, security-conscious, or governance-driven environments is

highly desirable

Key Skills

Education

Bachelor's degree