In this role you will:

- Focus on architectural, conceptual, communication, and organizational aspects of GenAI application development.

- Partner with business and technology stakeholders to define

GenAI application and solution architecture

, translating complex business challenges into scalable ML- and LLM-based designs (e.g. copilots, assistants, agents, smart workflows).

- Own end-to-end GenAI solution architecture

, covering data pipelines, retrieval-augmented generation (RAG), model orchestration, LLM integration, vector stores, prompt and agent frameworks, deployment patterns, observability, security, and governance.

- Architect and guide implementations on Microsoft Azure

, including selection and integration of services for GenAI workloads and applications (e.g.

Azure

OpenAI Service, Azure Machine Learning, Azure Kubernetes Service, Azure App Service, Azure Functions, Event Hubs / Service Bus, Cosmos DB, Azure SQL, Azure Storage, Azure Cognitive Search).

- Lead complex, cross-regional GenAI initiatives from use-case discovery and solution shaping to enterprise rollout, with a strong focus on business impact, reusability, and sustainability.

- Drive the GenAI agenda together with the team and the GenAI task force – from platform and reference architecture, governance and responsible AI, to capability building, enablement, and hands-on technical leadership in key projects.

- Validate feasibility and value of GenAI use cases through prototyping, architecture evaluations, PoCs, and design review s, providing clear technical guidance and recommendations.

- Define and promote architectural standards and reusable building blocks

(APIs, microservices, prompts, agents, templates) for GenAI applications to accelerate delivery across business units.

- Collaborate with security, compliance, and data privacy teams to ensure responsible and compliant GenAI solutions, addressing IP protection, data residency, and safety requirements.





- Define and enforce Azure deployment and DevOps best practices

, including infrastructure-as-code, environment strategy, security-by-design, monitoring, and cost optimisation.

- Design and oversee CI/CD pipelines for GenAI applications and services on Azure, leveraging GitHub and GitHub Actions (and/or Azure DevOps where applicable) for automated builds, testing, vulnerability scanning, and deployments across environments.

- Ensure robust release management practices

, including branching and tagging strategies, release approvals, rollback and recovery concepts, blue-green / canary deployments where appropriate, and clear traceability from requirements to production releases.

You have:

- Preferably a Master’s degree in a STEM subject (Science, Technology, Engineering, Mathematics), such as Data Science, Computer Science, Statistics, Mathematics, or a related field.

- 8+ years of experience in data science, AI, advanced analytics, or software engineering, with proven experience in solution or technical architecture roles for data-intensive or AI-powered applications.

- Deep expertise in machine learning, including supervised and unsupervised learning, along with strong experience in model lifecycle management, deployment, and scalability in production environments.

- Strong hands-on and architectural experience with GenAI technologies, including transformers, LLMs, diffusion models, multimodal AI, and common GenAI application patterns (RAG, agents, copilots, chatbots).

- Proven experience designing and delivering GenAI and AI/ML solutions on Microsoft Azure, including:





- Azure OpenAI Service and/or Azure Machine Learning for experimentation, training, and deployment

- Azure Cognitive Search / vector search, storage and data services (e.g. Cosmos DB, Azure SQL, Blob Storage)

- Integration and messaging services (e.g.

Event

Hubs, Service Bus, API Management)

- Excellent proficiency in Python and relevant libraries (e.g. NumPy, Pandas, scikit-learn, PyTorch/TensorFlow, statsforecast, LangChain or similar orchestration frameworks).

- Experience with cloud-native architectures and enterprise integration patterns (APIs, event-driven architectures, microservices).

- Demonstrated DevOps and CI/CD expertise on Azure, including:

- Designing and maintaining GitHub repositories with robust branching, code review, and collaboration practices

- Building and operating GitHub Actions (and/or Azure DevOps pipelines) for automated testing, security checks, and deployments

- Applying best practices for versioning, setting management, release procedures, and rollback strategies for GenAI and AI/ML solutions in Azure

- Using infrastructure-as-code tools (Terraform preferred) for repeatable, compliant environment provisioning

- MCP experience is preferred.

- Strong understanding of data architecture, from ingestion and feature engineering to semantic layers, vector databases, knowledge graphs, and analytical/consumption layers.

- Proven ability to manage complex, multi-stakeholder initiatives across regions, time zones, and priorities, aligning diverse stakeholders around a common architecture vision.

- Excellent communication skills in English, with the ability to explain complex technical concepts to both technical and non-technical audiences, including senior management.

- A collaborative mindset, strong ownership, strategic and product-thinking, and the confidence to challenge the status quo and advocate for pragmatic, scalable solutions.

📌 Solution Architect – GenAI Applications (Bengaluru)
🏢 Zeiss India
📍 Bengaluru