Position: AI Solution Architect – Azure AI Foundry
Experience: 10–15+ years overall IT experience with 5+ years in AI/ML, GenAI, or AI solution architecture
Location: Hyderabad
Employment Type: Full-Time
Role Overview: We are looking for an experienced AI Solution Architect with strong hands-on expertise in Generative AI, Large Language Models (LLMs), Azure AI Foundry, Azure OpenAI, RAG, and Agentic AI.
The candidate will be responsible for architecting enterprise-grade AI solutions on Microsoft Azure, translating business requirements into scalable AI architectures, and guiding engineering teams from proof of concept through production deployment.
The ideal candidate should combine AI/GenAI expertise, Azure cloud architecture, software engineering, data architecture, security, and strong stakeholder-management skills.
Key Responsibilities
AI Solution Architecture
- Design end-to-end enterprise GenAI and AI solutions using Azure AI services.
- Define scalable, secure, reliable, and cost-effective AI architectures.
- Translate business requirements and use cases into technical AI solutions.
- Evaluate AI models, frameworks, tools, and technologies and recommend appropriate solutions.
- Create architecture diagrams, technical designs, POCs, and reference architectures.
- Establish architecture standards and best practices for AI application development.
Azure AI Foundry & Azure OpenAI
- Lead implementation of solutions using Azure AI Foundry.
- Design and deploy applications using Azure OpenAI Service and foundation models.
- Work with Azure AI Search, Azure Machine Learning, Azure Storage, Azure Functions, and other Azure services.
- Establish appropriate model selection, deployment, security, and governance strategies.
- Optimize AI workloads for performance, scalability, reliability, and cost.
Generative AI, RAG & Agentic AI
- Architect RAG (Retrieval-Augmented Generation) solutions using enterprise data.
- Design AI Agent and Agentic AI architectures, including multi-agent workflows where appropriate.
- Define approaches for embeddings, vector search, semantic retrieval, and knowledge grounding.
- Design prompt engineering and context-management strategies.
- Evaluate LLMs based on accuracy, latency, cost, security, and business requirements.
- Design LLM evaluation and monitoring strategies.
Data & Integration Architecture
- Define data architecture required for AI/GenAI applications.
- Integrate structured and unstructured enterprise data with AI solutions.
- Design APIs, microservices, event-driven integrations, and enterprise application interfaces.
- Work closely with data engineering teams on data pipelines and data quality.
- Experience with Microsoft Fabric, Azure Data Factory, Databricks, or equivalent is an advantage.
AI Security & Responsible AI
- Define security architecture for enterprise AI applications.
- Implement data privacy, access control, encryption, and secure API integration.
- Establish AI governance, responsible AI, and compliance practices.
- Address risks such as prompt injection, data leakage, hallucination, and unauthorized model access.
- Define guardrails and content-safety mechanisms for GenAI applications.
DevOps & Productionization
- Define CI/CD strategies for AI applications.
- Work with Docker, Kubernetes, Git, and Azure DevOps/GitHub.
- Establish deployment, monitoring, logging, and observability practices.
- Guide teams in moving AI POCs into scalable production solutions.
- Define operational processes for model and application lifecycle management.
Leadership & Stakeholder Management
- Work with business leaders, product owners, engineering teams, data scientists, and cloud architects.
- Lead technical discussions and architecture reviews.
- Mentor AI/ML engineers and development teams.
- Communicate complex AI concepts clearly to technical and non-technical stakeholders.
- Provide technical leadership across multiple AI initiatives.
Mandatory Technical Skills
AI / GenAI
- Generative AI
- Large Language Models (LLMs)
- RAG
- Agentic AI / AI Agents
- Prompt Engineering
- Embeddings
- Vector Search / Vector Databases
- LLM Evaluation
- AI Guardrails
- Responsible AI
Microsoft Azure
- Azure AI Foundry
- Azure OpenAI Service
- Azure AI Search
- Azure Machine Learning
- Azure Functions
- Azure App Services
- Azure Storage
- Azure Cloud Architecture
Programming & Engineering
- Strong Python
- REST APIs
- Microservices
- Docker
- Kubernetes
- CI/CD
- Git / GitHub / Azure DevOps
Good-to-Have Skills
- Microsoft Fabric
- Azure Data Factory
- Databricks
- Semantic Kernel
- LangChain / LangGraph
- AI Agent frameworks
- Power BI
- MLOps / LLMOps
- AI security and governance
- Enterprise architecture frameworks
Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field.
- 10–15+ years of overall technology experience.
- 5+ years of experience in AI/ML, GenAI, or AI solution architecture.
- Proven experience designing and delivering enterprise AI solutions.
- Strong communication, presentation, and stakeholder-management skills.