Fractal
Website:
fractal.ai
Job details:
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Lead Architect - Azure & Agentic AI Solutions
Architecture leadership for Azure deployment, agent design, orchestration and end-to-end solution flow
Role Overview
We are seeking a Lead Architect to define the target architecture for an enterprise-grade procurement agentic AI solution. The role will determine the appropriate Azure deployment stack, establish architecture principles and guardrails, and advise on agent architecture, orchestration patterns, integration design, and end-to-end solution flows. The Lead Architect will work closely with client technology stakeholders, the Solution Lead, AI/ML teams, data engineers, security teams and delivery leadership to ensure the solution is scalable, secure, operable and fit for enterprise deployment.
Key Responsibilities
- Own the end-to-end target architecture across data, AI/ML, GenAI, agent orchestration, APIs, integration, observability, security and user-facing application layers.
- Assess client constraints and determine the appropriate Azure deployment stack, including services for compute, storage, data processing, model serving, GenAI, integration, secrets, networking, monitoring and CI/CD.
- Define cloud deployment patterns across Azure OpenAI, Azure AI Foundry, Azure Machine Learning, Databricks or equivalent data platforms, AKS, Container Apps, Functions, API Management, Service Bus or Event Grid, Key Vault, Monitor and related services as appropriate.
- Advise on when to use agents, conventional services, rules engines, workflow orchestration, ML models, optimization services and event-driven automation rather than forcing all use cases into an agent pattern.
- Translate business workflows into executable solution flows spanning sensing, signal processing, forecasting, recommendation, approval, action and traceability layers.
- Define API and integration architecture for interaction with enterprise source systems, planning tools, data platforms, UI applications, model endpoints and external services.
- Establish non-functional requirements and architecture guardrails for scalability, latency, resilience, disaster recovery, cost efficiency, explainability, auditability and supportability.
- Define identity and access patterns, network segmentation, secrets management, data protection controls, content safety, prompt security and responsible AI controls in collaboration with client security teams.
- Lead architecture reviews, design walkthroughs, technical decision records and trade-off discussions with internal and external stakeholders.
- Provide hands-on guidance to engineering teams on implementation patterns, reusable components, deployment standards and technical issue resolution.
- Own architecture alignment across environments including development, test, UAT, and production, with clear promotion, rollback and release patterns.
- Guide observability design across application traces, agent traces, prompt and tool execution, model performance, API metrics, data pipeline health, cost usage and business process outcomes.
- Support capacity planning and cloud cost optimization, including workload sizing, token consumption controls, autoscaling, caching, batching and appropriate service selection.
- Collaborate with the Lead Manager and Solution Lead to balance delivery timelines with architecture quality and enterprise readiness.
Required Skills & Experience
- 10+ years of experience in enterprise solution architecture, cloud architecture, platform engineering or related technology leadership roles.
- Strong hands-on architecture experience with Microsoft Azure and enterprise deployment patterns.
- Proven experience designing distributed systems, APIs, microservices, event-driven architectures, containerized workloads and cloud-native integrations.
- Strong understanding of GenAI and agentic AI architecture, including LLM orchestration, tool calling, RAG, state and memory patterns, evaluation, guardrails and human approval mechanisms.
- Ability to design architectures that combine conventional software services, AI/ML models, optimization engines, data pipelines and agentic workflows.
- Strong knowledge of security, identity, networking, observability, CI/CD, infrastructure-as-code and production operations in enterprise environments.
- Experience engaging with client architects, CIO/CTO organizations, cloud platform teams, security stakeholders and delivery teams.
- Strong ability to communicate architecture trade-offs to both technical and business stakeholders and drive decisions under real-world constraints.
Preferred Qualifications
- Experience with Azure OpenAI, Azure AI Foundry, Azure Machine Learning, Databricks, AKS, Container Apps, API Management, Service Bus, Event Grid, Key Vault, Entra ID and Azure Monitor.
- Experience with agent orchestration frameworks such as LangGraph, Semantic Kernel, AutoGen, or equivalent patterns and frameworks.
- Exposure to supply chain, demand forecasting, planning, semiconductor or high-tech industry solutions.
- Experience with MLOps, LLMOps, model monitoring, prompt and agent evaluation and production AI governance.
- Relevant Azure architecture certifications or equivalent demonstrable experience.
Key Deliverables
- Target-state solution architecture and Azure service deployment blueprint.
- Agent architecture and orchestration design, including tool boundaries and control flows.
- End-to-end sequence flows for key business scenarios and exception paths.
- Integration, API, identity, networking, security, observability and environment architecture.
- Architecture decision records, non-functional requirements, deployment standards and engineering guardrails.
- Production-readiness and scalability recommendations, including cost and operational considerations.
Key Success Metrics
- Architecture is accepted by client technology and security stakeholders with minimal late-stage redesign.
- Solution is deployable, scalable, secure, observable and supportable across enterprise environments.
- Agent and orchestration patterns remain controlled, explainable and appropriate to the business workflow.
- Reduction in integration rework, deployment friction, technical debt and avoidable cloud cost.
- Engineering teams can implement consistently using clear architecture standards and reusable patterns.
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