Azure AI Architect (Technical Advisor Specialist ) Role - Blr / Hyd

Job description

Greetings!!!! Currently we have an urgent Position for Azure AI Architect Role with one of our projects, location based on Bangalore / Hyderabad. Kindly find below the Details for your Perusal. Job Location               : Bangalore / Hyderabad Mode of Employment : Permanent (Work Mode: Hybrid, weekly 2 days in office) Shift Mode: Rotational Shift which includes night Shift as well. Job Description: RESPONSIBILITIES SUMMARY: 1. Startup Discovery & AI Readiness Assessment • Lead startup-focused discovery sessions to understand the founder vision, product roadmap, customer use cases, and near-term GTM priorities. • Assess AI readiness across data availability, engineering maturity, cloud footprint, and cost constraints common to early-stage startups. • Help startups identify high-impact AI use cases that accelerate product differentiation, customer value, or operational efficiencyavoiding over-engineering. • Guide founders and engineering leaders on when to use Copilot, Azure OpenAI, Azure AI services, or custom ML, balancing speed, cost, and scalability. 2. AI Architecture Design for Startup Scale • Design lean, scalable AI architectures using Azure OpenAI, Azure AI Studio, Azure ML, Cognitive Services, and Azure-native data platforms. • Define MVP-first AI patterns (RAG, prompt engineering, inference-only architectures) optimized for rapid iteration and fast customer validation. • Create future-ready architecture that allows startups to scale from pilot to production without rework as usage and customers grow. • Provide architecture diagrams, reference patterns, and decision rationale that startup teams can easily execute against. 3. Azure Credits, Quotas & Cost-Conscious AI Design • Advise startups on Azure credits usage strategy, ensuring AI workloads are aligned to available funding and program entitlements. • Guide startups through Azure OpenAI / GPU quota planning, helping unblock capacity constraints and avoid design dead-ends. • Recommend cost-optimized AI approaches (model selection, inference strategies, batch vs real-time, caching, vector store design). • Help startups understand unit economics of AI features, connecting architecture decisions to burn rate and runway. 4. Hands-on Technical Advisory & PoCs • Provide hands-on advisory support to startup engineering teams during build phases, not just high-level guidance. • Lead or review proofs of concept (PoCs) to validate AI feasibility, latency, cost, and user experience early. • Review startup implementations for architecture soundness, security basics, and scalability risks, offering pragmatic improvements. • Support integration of AI into existing product stacks (APIs, web apps, mobile apps, SaaS platforms). 5. Responsible AI, Security & Trust (Startup-Appropriate) • Embed Responsible AI principles in a way that is practical for startupsfocused on trust, transparency, and customer confidence. • Advise on data handling, PII protection, and secure model access, especially for startups entering enterprise or regulated markets. • Help startups prepare for enterprise customer security reviews by aligning early with Azure and Microsoft security best practices. 6. Enablement & Founder / Team Upskilling • Upskill startup teams on Azure AI services, OpenAI patterns, and production-ready AI design through working sessions and reviews. • Share reusable reference architectures, templates, and best practices to accelerate repeatable AI delivery. • Support creation of internal AI standards or lightweight AI governance as startups mature. • Act as a long-term technical advisor, helping startups evolve their AI approach as product-market fit and scale change. 7. Microsoft for Startups Program Alignment • Align AI architecture recommendations with Microsoft for Startups goals: faster Azure adoption, sustainable scale, and long-term customer success. • Collaborate with Microsoft account teams, program managers, and partners to unblock startups and maximize program value. • Provide clear, outcome-focused summaries for internal stakeholders highlighting progress, risks, and next advisory actions. • Identify patterns, blockers, and common challenges across startups to help improve program effectiveness. How this role is distinct in Microsoft for Startups Compared to enterprise AI architects, this role is: • More hands-on, less theoretical • Cost- and credit-aware • MVP- and speed-focused • Founder- and product-centric • Designed for rapid iteration, not long transformation cycles SKILLS: • Understanding of, or curiosity to ramp up on, Azure AI/ML infrastructure, platform, and AI/ML services on L200-300 level: • Infrastructure planning for running and Finetuning LLM’s on managed compute. • Performance optimization techniques for inferencing workloads. • Integration of Azure ML with data Analytics platforms like Azure Synapse analytics or Databricks. • Distributed model training in Azure Machine Learning. • Designing Recommendation/personalization models. • Implementing observability and monitoring on Azure AI/ML services. • Deep understanding of Azure services (Azure Machine Learning, Azure Cognitive Services, Azure Synapse Analytics/Databricks, etc.) and building solutions around these services. • Proficiency in AI and ML frameworks and tools (TensorFlow, PyTorch, Scikit-learn, etc.). • Good understanding of frameworks like Semantic Kernel, Autogen, Langchain and protocols like MCP and Agent to Agent. • Good understanding of data engineering and ETL processes. • Experience of having handled ML specific requests and/or solution build for startups • Ability to understand and deep dive on ML pipeline, ML Ops and data ingestion as it refers to Azure ML. • Ability to gear up on applied AI services like Azure OpenAI