Role Title: Technical Advisor Specialist(AI) Microsoft for Startups Founders Hub is a dynamic platform that supports founders from the moment they have an idea. As their startup grows, founders unlock new benefits and features that help them hit their technical milestones and tackle business challenges. Founders globally can sign up for Microsoft for Startups Founders Hub and start using market-leading AI tools and a suite of benefits tailor-made to their startup stage with no funding required.

Microsoft Services help customers realize their full potential through accelerated adoption and productive use of Microsoft technologies. We are a global team of highly dedicated professionals who deliver world class services with partners, earning customer confidence, trust, and loyalty by improving the overall Customer and Partner Experience, serving as the customer most trusted advisors within Microsoft and driving customer-centric product improvement.

Azure Technical

Advisory support is a Microsoft services & support offering targeted at Microsoft for Startups Founders Hub startups. This is a customer-specialist facing role and would require the individual to act as the customer’s trusted Technical Advisor Specialist.

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 efficiency—avoiding 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.

- 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 rapid 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.

- 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).

- Responsible AI, Security & Trust (Startup-Appropriate)

Embed Responsible AI principles in a way that is practical for startups—focused 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.

- 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.

- 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 Service on L300 and consult with startups about RAG, fine-tuning, prompt engineering, building Agentic systems etc. Ability to understand the magnitude of ML pipeline in terms of data set size, intensive training, computing involved etc, to have a planning discussion with the startup.

Ability to carry out weekly discussions and report on highs, lows and blockers. Requirements:

- Bachelor's degree in Computer Science or a related field
- Minimum of 12+ years of experience in Azure cloud computing and 3+ years in Open AI technology
- Experience in designing and implementing solutions using Azure AI services
- Strong understanding of Azure cloud services, including Azure Machine Learning and Cognitive services
- Proficiency in programming languages such as Python, C#, and Java and Proficiency in SQL
- Excellent problem-solving skills and ability to think creatively
- Strong communication skills and ability to work with clients
- Ability to work independently and in a team environment
- Relevant Azure certifications preferred (AZ-104, AZ-305, AI-102, AB-730 & AB-731 certification, preferred)

📌 Technical Advisor Specialist(AI) (Hyderabad)
🏢 Sonata Software
📍 Hyderabad