Enterprise Architect

Enterprise Architect Introduction to role Are you ready to architect enterprise-scale AI that accelerates how we discover and deliver life-changing medicines? Do you want to connect emerging AI capabilities with business ambitions to simplify the enterprise landscape and unlock measurable value for patients and colleagues?

This hands-on enterprise architecture role blends leadership with deep AI/ML engineering. Based in Bangalore with a global remit, you will define our enterprise AI architecture strategy and design, build, and scale platforms across AWS, Microsoft Azure, and Databricks.

You will drive agentic AI, foundation models, and MCP adoption, ensuring alignment with our data strategy and the rigor required in a regulated environment. You will work end to end across business and technology domains, challenge processes to get the best results, and move at pace to deliver outcomes that matter!

Accountabilities Enterprise AI Strategy: Define AI architecture and reference standards aligned to business and R&D priorities, translating strategic intent into executable roadmaps that deliver value. Platform Architecture: Architect scalable AI platforms across AWS, Microsoft Azure, Databricks, and hybrid environments, enabling secure, compliant, and efficient AI delivery.

Agentic and Foundation Models: Lead architecture for agentic AI, multi-agent orchestration, LLMs, multimodal AI, RAG, and MCP, selecting fit-for-purpose patterns per use case. Advanced Capabilities: Design vector databases, knowledge systems, and end-to-end AI integration that connect data, models, and applications to business outcomes.

Responsible AI: Embed governance, compliance, security, and risk management across designs, ensuring traceability, fairness, and regulatory readiness. Hands-on AI/ML Engineering: Build, fine-tune, and deploy ML/DL models with strong MLOps across the full AI lifecycle, proving patterns through production-grade exemplars.

Design Ownership: Own AI designs across one or more large or complex business capabilities, setting standards and assuring consistency across domains. Quality Assurance: Produce architecture blueprints and review designs from AI model and solution architects to raise quality and reduce technology debt.

Data Governance Partnership: Partner with Data Offices on data governance, master-data usage, and classification to maximize reuse and reliability. Leadership and Mentoring: Mentor AI architects, manage work packages and demand planning, and coordinate with enterprise architecture partners to amplify impact.

Vendor and Partner Engagement: Shape AI roadmaps with vendors and outsourcers, negotiate target patterns, and guide the enterprise landscape vision. Global Collaboration: Work with AI stakeholders, business unit leaders, and sponsors across all countries to scale reusable patterns and accelerate adoption.

Essential Skills/Experience - Enterprise Architecture experience: proven EA leadership translating concepts into production-ready solutions. - Hands-on AI/ML engineering: building, fine-tuning, and deploying ML/DL models (LLMs, RAG, MLOps) in production. - AI platforms: hands-on with AWS (Bedrock, SageMaker, Amazon Q), Azure (Azure AI, ML, OpenAI), and Databricks. - Engineering and analytics: strong Python, TensorFlow/PyTorch, containers, Kubernetes, and CI/CD for hybrid cloud. - Data modelling and governance: conceptual/logical modelling and governance standards in regulated environments. - Architecture judgement: select fit-for-purpose AI architecture per use case, with full-lifecycle understanding. - Leadership: lead a small team of AI architects and help shape enterprise AI strategy and direction. - Degree in data science, AI engineering, or a related field (or equivalent experience).

Desirable Skills/Experience - Postgraduate degree in MIS, AI, data science, or a related field. - Recognised thought leader in applying AI within the enterprise and across the industry. - Extensive senior AI, data science, data engineering, and AI architecture experience delivering large-scale blueprints. - Hands-on building AI models, including LLMs and LVMs, across diverse data types. - Agile AI delivery experience; tools for metadata cataloguing, data modelling, and enterprise architecture. - Experience in the pharmaceutical AI industry. - Ability to simplify the enterprise landscape and reduce technology debt.

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office.

But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world. Why AstraZeneca Here, you will work across domains to connect trends with business ambitions, simplify how we operate, and challenge processes to deliver the best value.

You will collaborate with diverse experts, learn at speed, and build AI platforms that uplift business capabilities and ultimately benefit patients. We blend ambition with kindness, placing unexpected teams in the same room to unleash bold thinking, and we support you with the freedom to explore, iterate, and see your ideas land in production at global scale.

Step into this role to shape our enterprise AI architecture and create measurable impact for patients and the business—submit your application today and help define what’s next! Date Posted 16-Jul-2026 Closing Date 29-Jul-2026 AstraZeneca embraces diversity and equality of opportunity.

We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be.

We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.