The Role: Azure AI Solutions Architect

We are looking for a hands-on Azure AI Solutions Architect to design, prototype, and pitch AI solutions built on Microsoft Azure. You will own the technical story end to end — running discovery workshops with customers, shaping the architecture, building the proof of concept that proves it works, and presenting it to business and technical stakeholders. You will work closely with the CTO and the presales team to convert opportunities into funded, delivered engagements.

This is a builder-and-presenter role. We expect you to be genuinely hands-on with Azure AI services — able to stand up a working RAG or agent prototype yourself — and equally comfortable in front of a bank CIO explaining why the design is the right one, what it costs, and how it will be governed.

Key Responsibilities

AI Solution Architecture

■ Design end-to-end AI solutions on Azure — Azure OpenAI, Azure AI Foundry, Azure AI Search, Document Intelligence, Speech, Vision, and Language services.

■ Architect retrieval-augmented generation (RAG) patterns — chunking and embedding strategy, vector and hybrid search, grounding, evaluation, and feedback loops.

■ Design agentic and multi-agent solutions using Azure AI Foundry Agent Service, Semantic Kernel, or Copilot Studio, including tool / function calling and orchestration.

■ Produce reference architectures, solution blueprints, sizing models, and cost estimates aligned to CloudGate standards and the Microsoft Cloud Adoption and Well-Architected Frameworks.

Select the right pattern for the problem — prompt engineering, RAG, fine-tuning, or classical ML — and be able to defend the choice technically and commercially.

Presales & Solution Pitching

■ Lead technical discovery workshops with customers to qualify AI use cases, map them to business value, and shape the scope.

■ Build and deliver compelling demos, prototypes, and proofs of concept that show the solution working on the customer's own data.

■ Write and present solution proposals, technical scopes of work, bills of quantity, and architecture decks to CIO, CDO, and business stakeholders.

■ Support Microsoft co-sell motions — engage Microsoft account teams, position solutions against Microsoft funding programmes (MCI / ECIF / AI Design Wins), and prepare the supporting artefacts.

■ Respond to RFPs, RFIs, and security questionnaires with clear, technically accurate answers.

Data Foundations & Integration

■ Design the data layer behind AI workloads — Microsoft Fabric, OneLake, Azure Data Lake, Azure SQL,



Cosmos DB, and medallion architectures.

■ Build ingestion and enrichment pipelines for structured and unstructured content, including OCR and document processing with Azure AI Document Intelligence.

■ Integrate AI solutions with enterprise systems — Microsoft 365 and Graph, Dynamics 365, core banking, ERP, and line-of-business APIs.

■ Prototype in Python (and TypeScript where useful) using the Azure AI SDKs, Semantic Kernel, or LangChain.

Responsible AI, Security & Governance

■ Apply Microsoft Responsible AI principles — content safety, groundedness, evaluation, red-teaming, and human-in-the-loop design. Design for enterprise security — private endpoints, managed identity, Entra ID authentication, RBAC, customer-managed keys, and data residency requirements across UAE, Saudi Arabia, and African markets.

■ Define model evaluation and monitoring approaches — quality, latency, token cost, drift, and abuse detection.

■ Address data governance and compliance expectations with Microsoft Purview and customer regulatory requirements (central bank, data protection, and sector-specific rules).

Delivery & Handover

■ Stay engaged through delivery — guide the engineering team, review implementation against the design, and resolve technical blockers.

■ Document architectures, design decisions, evaluation results, and operational runbooks clearly enough for another team to run with them.

■ Contribute to CloudGate's reusable AI accelerators, demo assets, and solution catalogue.

Support customers through UAT, go-live, and post-deployment tuning.

What You Bring (Must-Have)

■ 3 – 5 years of hands-on experience in AI/ML, data, or cloud solution engineering, with meaningful recent delivery on Microsoft Azure.

■ Strong, demonstrable experience with Azure OpenAI and Azure AI Foundry — deployments, model selection, prompt engineering, and evaluation.

■ Practical experience building RAG solutions with Azure AI Search or a comparable vector store — you have shipped one, not just read about it.

■ Solid Python skills and comfort with the Azure AI SDKs, REST APIs, and at least one orchestration framework (Semantic Kernel,



LangChain, or LlamaIndex).

■ Working knowledge of Azure core services underpinning AI workloads — App Service or Container Apps, Functions, Key Vault, Storage, Networking and Private Endpoints, Entra ID, and Log Analytics.

■ Experience with data platforms — Microsoft Fabric, Synapse, Databricks, or equivalent — and a good grasp of data modelling for AI.

■ Proven customer-facing ability — you can run a workshop, present an architecture, and handle challenge from technical and business stakeholders.

■ Ability to produce high-quality written deliverables — architecture documents, proposals, and decks — with minimal rework.

■ Robust written and spoken English; able to communicate clearly with customers, Microsoft, and internal teams.

■ Bachelor's degree in Computer Science, Data Science, IT, Engineering, or a related field.

Owner's mindset — you ship what you commit to, document what you build, and flag risks early

Advantage to have:

■ Microsoft Certified: Azure AI Engineer Associate (AI-102) or Azure Solutions Architect Expert (AZ-305).

■ Microsoft Certified: Fabric Analytics Engineer (DP-600) or Fabric Data Engineer (DP-700).

■ Microsoft Certified: Azure Data Scientist Associate (DP-100) or Azure Security Engineer Associate (AZ-500).

■ Experience with Microsoft 365 Copilot, Copilot Studio, or GitHub Copilot in an enterprise deployment or presales context.

■ Exposure to MLOps / LLMOps — CI/CD for AI workloads, prompt versioning, evaluation pipelines, and Azure Machine Learning.

■ Infrastructure-as-code experience with Terraform or Bicep for deploying AI landing zones.

■ Experience with speech, translation, or Arabic-language NLP scenarios.

Prior presales or delivery experience with banking, fintech, telco, energy, or public sector customers in MENA or Africa.

What We Offer

■ Fully remote — work from home in India with flexible hours aligned to MENA business overlap.

■ Real enterprise AI engagements from day one — live customer data, live stakeholders, no dummy projects.

■ Direct mentorship from the CTO and senior architects, and a seat at the table on solution strategy.

■ Sponsored Microsoft certifications (AI-102, AZ-305, DP-600, DP-700, AZ-500) and a structured technical growth plan.

■ Competitive compensation, performance bonus, and a clear path to Senior AI Architect and Practice Lead roles.

A culture that values ownership, engineering quality, and shipping working software

📌 Azure AI Solutions Architect (India)
🏢 CloudGate
📍 India