Solution Architect – GenAI Applications
ZEISS India
- Location
- Bengaluru, Karnataka, India
- Job type
- Full-time
Required skills
- LangChain
- Python
- advanced analytics
- automated testing
- Azure
- CI
- code review
- communication skills
- compliance
- data architecture
- data science
- design review
- DevOps
- end-to-end
- GitHub
- infrastructure-as-code
- Kubernetes
- machine learning
- microservices
- NumPy
- Pandas
- reference architecture
- Reference Architecture
- SQL
- statistics
- technical architecture
- TensorFlow
- Terraform
- Pytorch
About the role
ZEISS India
Website:
zeiss.co.in
Company:
https://www.linkedin.com/company/carl-zeiss-india-czi
Seniority: Director
Industries: Manufacturing
Job details:
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 reviews, 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, environment 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.
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