Solution Architect - Terraform,Python,Google Cloud Build,Ansible

Job Summary

Position Summary We are looking for an AI & Automation Architect who can take an idea from business problem to production platform. This is an end-to-end architecture role covering AI, GenAI, LLMs, agentic systems, automation, AIOps, cloud, security and enterprise infrastructure. The architect will define the target architecture, select models and platforms, design data and integration layers, establish security and governance, guide engineering teams and stay involved through build, deployment and operations. The objective is an AI-native operating model across Data Center, Network, Cyber Security, Workplace/EUC, Service Management and Hybrid Cloud-not a collection of disconnected AI pilots.

Key Responsibilities

What You’ll Own

• Own the enterprise AI and automation architecture, standards, reference patterns and technology roadmap.
• Translate business and operational problems into practical AI, automation and agentic solutions with measurable outcomes.
• Lead architecture from discovery and use-case selection through design, POC, production, adoption and lifecycle management.
• Decide where deterministic automation, GenAI, agents, AIOps, ML, workflow or traditional engineering is the right answer.
• Create reusable architecture patterns so delivery teams can build safely without reinventing the platform.
• Work across Infrastructure, Network, Security, Workplace, Cloud, Service Management and Enterprise Architecture.
• Remain technically hands-on: prototype, review designs/code, run technology evaluations and unblock engineering teams. LLM & Foundation Model Architecture
• Design the LLM lifecycle: model selection, data preparation, prompting, fine-tuning, evaluation, deployment, serving, monitoring and retirement.
• Work with proprietary and open LLMs, SLMs and multimodal models; assess quality, context, latency, cost, privacy and operational fit.
• Understand fine-tuning, instruction tuning, LoRA/PEFT, quantisation, distillation and inference optimisation.
• Design model gateways and routing across cloud, private and on-prem models based on workload, risk, performance and cost.
• Architect GPU/accelerator capacity, inference endpoints and scalable model serving when self-hosted models are required.
• Define evaluation datasets and quality gates for groundedness, relevance, safety, hallucination, latency and cost. GenAI, RAG & Knowledge Architecture
• Architect enterprise RAG and Agentic RAG across documents, CMDB, tickets, telemetry, runbooks, APIs and operational data.
• Define ingestion, chunking, metadata, embeddings, vector stores, semantic/hybrid search and reranking.
• Design grounded knowledge services with access-aware retrieval, provenance and traceability.
• Use context engineering, memory, structured outputs and tool selection for production AI applications.
• Use knowledge graphs or graph retrieval where topology, dependency and relationships matter. Agentic AI & Autonomous Systems
• Design single-agent and multi-agent architectures for NOC, SecOps, Data Center, Workplace and Service Management.
• Define agent roles, planning, reasoning boundaries, memory, tools, policies, approvals and escalation.
• Use MCP, A2A, function/tool calling and event-driven orchestration where they add real value.
• Build agents that can observe, diagnose, recommend, execute and validate actions through controlled tools.
• Use confidence, risk and policy gates to determine autonomous versus human-approved execution.
• Design agent observability for prompts, model calls, retrieval, tool calls, decisions, failures, latency and cost.
• Build closed-loop patterns: detect → understand → decide → act → validate → learn. Microsoft AI & Copilot Architecture
• Microsoft Copilot, Copilot Studio and enterprise agent architecture.
• Microsoft Foundry and Azure OpenAI for enterprise AI, model and agent workloads.
• Power Platform, Power Automate, Logic Apps, Azure Functions and API Management for orchestration.
• Secure agents connected to ServiceNow, Microsoft 365, Entra ID, Intune, network, security and infrastructure platforms.
• Define reusable agent skills, tools, connectors, prompts, knowledge sources and governance patterns. AI-Native Infrastructure Architecture
• Data Center: VMware, Hyper-V, Azure Local/Azure Stack HCI, HCI, Windows, Linux/Unix, Active Directory, storage, backup, DR and Cohesity.
• Network: Aruba CX/Central/ClearPass, Wireless, Palo Alto NGFW/Panorama, WAN, SD-WAN, VXLAN/EVPN and cloud networking.
• Workplace: Microsoft 365, Intune, Autopilot, Entra ID, Defender, Citrix/EUC and DEX.
• Security: Zero Trust, NAC, micro-segmentation, SASE/SSE, SIEM/SOAR, NDR, IAM/PAM and security automation.
• Design service-aware automation using

Skill Requirements

Experience

• 15+ years in enterprise architecture, infrastructure, cloud, automation, software engineering or related technology roles, with substantial hands-on delivery.
• Proven experience designing and delivering enterprise AI/GenAI or automation platforms-not only strategy or POCs.
• Strong understanding of network, security, data center, workplace and cloud operations.
• Experience taking AI/automation from business case and architecture through engineering, production rollout and adoption.
• Strong communication and architecture governance skills across engineers, architects, executives and business stakeholders.

Other Requirements

Preferred Certifications

• Microsoft Azure Architect / AI Engineer / DevOps / Security or equivalent.
• AI/ML, GenAI or relevant cloud AI credentials.
• Terraform and Red Hat Ansible Automation.
• VMware, Aruba and Palo Alto professional certifications.
• TOGAF or equivalent enterprise architecture qualification.
• ITIL / ServiceNow, SRE or cloud architecture credentials are a plus.