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.

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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 transparent, 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)

About Sonata Software:

Sonata is a global software company, one of the fastest growing in India. It specializes in Platform Engineering, Sonatas proprietary Platformation methodology, providing a framework that assists companies with their digital transformation journeys and helps them build their businesses on platforms that are Open, Connected, Intelligent, and Scalable. Sonata's platform engineering expertise is supported by its capabilities in Cloud and Data, IoT, AI, and Machine Learning, Robotic Process Automation, and Cybersecurity. With its centres in India, the US, Europe, and Australia, Sonata brings Thought leadership, Customer-centricity, and Execution Excellence towards catalysing the business transformation process for its customers around the world.

You can read more here: https://www.sonata-software.com/platformation

Best Regards,

Sathish Kumar Gnanasekaran

Talent Acquisition

M: +91 (phone hidden)

📌 Azure AI Architect (Technical Advisor Specialist ) Role - Blr / Hyd (Bengaluru)
🏢 Sonata Software
📍 Bengaluru