AI Azure Architect

Role : AI Azure Architect

Overview & Expectations :

Role Summary :

- Design, build, lead, and deliver production-grade AI solutions on Azure.

- Own execution excellence with measurable business value, technical depth, governance, and reliability.

Key Outcomes (06 to 12 months) :

- Ship production-grade AI/GenAI solutions with clear ROI, reliability (SLOs), and security.

- Establish engineering standards, CI/CD pipelines, observability, and repeatable delivery patterns.

- Build a reusable AI platform that enables AI applications across multiple domains (paved paths, templates, guardrails).

- Mentor engineers via reviews, playbooks, and hands-on guidance.

Responsibilities :

- Translate business problems into well-posed technical specifications and architectures.

- Lead design reviews, prototype quickly, and harden solutions for scale (high QPS / 1M+ users).

- Build automated pipelines (CI/CD) and model/data governance across environments (dev/test/prod).

- Define and track KPIs : accuracy, latency, cost, adoption, and compliance readiness.

- Partner with Product, Security, Compliance, and Ops to land safe-by-default systems.

GenAI + Agentic AI on Azure (must-have focus) :

- Implement Azure OpenAI solutions (prompting, evals, fine-tuning where applicable, safety filters).

- Build RAG architectures using Azure AI Search (vector) + curated data sources (SharePoint, SQL, Blob/ADLS, APIs).

- Design agentic workflows (tool use, multi-step orchestration, human-in-the-loop) using combinations of :

a. Azure Functions / Durable Functions, Logic Apps, Event Grid, Service Bus

b. Frameworks like Semantic Kernel / LangChain (as orchestration layer)

- Implement observability for agent workflows (traces, latency breakdown, failure modes, cost per run).

Technical Skills (Azure-focused) :

Platform & Runtime :

- Azure Kubernetes Service (AKS), Docker, Helm; Azure Container Registry (ACR)

- API Management, ingress patterns, autoscaling, secure networking (VNet, Private Link)

MLOps :

- Azure Machine Learning (pipelines, registries, endpoints), MLflow (tracking/registry)

- CI/CD with Azure DevOps or GitHub Actions, environment promotion, canary/champion-challenger patterns

Serving :

- Azure ML managed online endpoints and/or AKS-based inference

- FastAPI/gRPC-based services; performance tuning for low-latency inference

Data & Feature :

- ADLS Gen2, Azure Data Factory, Synapse/Databricks (as applicable)

- Feature store approach (Feast/managed equivalents), batch vs streaming (Event Hubs/Stream Analytics)

Monitoring & Observability :

- Azure Monitor, Application Insights, Log Analytics; Prometheus/Grafana where needed

- Model/data drift monitoring and alerting (Azure ML monitoring patterns)

Security & Compliance :

- Microsoft Entra ID (Azure AD), RBAC, Managed Identities, Key Vault

- Encryption at rest/in transit, network isolation, audit logging, policy controls

Hands-on programming :

- Strong applied coding in Python (plus scripting/automation).

Architecture & Tooling Stack :

- Git, branching standards, PR reviews, trunk-based delivery

- IaC : Bicep / Terraform (preferred), policy-as-code, reusable modules

- Registries/lineage/versioning and staged promotions for data/models

- Must have designed and built at least 3 Agentic AI solutions on Azure (end-to-end, production-grade).

Performance, Reliability & Cost :

- Define SLAs/SLOs for accuracy, tail latency, throughput, availability

- Capacity planning, autoscaling, load tests, caching, graceful degradation

- Cost controls : instance sizing, reserved/spot strategies, storage tiering

Qualifications :

- Bachelors/Masters or equivalent practical experience

- Proven track record of shipping and operating systems in production

- Must have strong platform engineering experience