Data Architecture (Agentic AI)

By clicking the “Apply” button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda’s Privacy Notice and Terms of Use . I further attest that all information I submit in my employment application is true to the best of my knowledge. Job Description The Future Begins Here At Takeda, we are leading digital evolution and global transformation. By building innovative solutions and future-ready capabilities, we are meeting the need of patients, our people, and the planet. Bengaluru, the city, which is India’s epicenter of Innovation, has been selected to be home to Takeda’s recently launched Innovation Capability Center. We invite you to join our digital transformation journey. In this role, you will have the opportunity to boost your skills and become the heart of an innovative engine that is contributing to global impact and improvement. At Takeda’s ICC we Unite in Diversity Takeda is committed to creating an inclusive and collaborative workplace, where individuals are recognized for their backgrounds and abilities they bring to our company. We are continuously improving our collaborators journey in Takeda, and we welcome applications from all qualified candidates. Here, you will feel welcomed, respected, and valued as an important contributor to our diverse team. The Opportunity The Data Architect (Agentic AI) is responsible for designing and evolving Takeda's next-generation enterprise data and AI architecture leveraging the Databricks Data Intelligence Platform, AWS, and Agentic AI technologies. This role will define scalable architecture patterns, AI-ready data platforms, and intelligent automation frameworks that enable trusted, governed, and autonomous AI solutions across the enterprise. Working closely with Enterprise Architecture, Data Engineering, AI Engineering, and business stakeholders, you will establish modern Lakehouse architectures that power Data Products, Generative AI, Agentic AI, analytics, and enterprise decision intelligence. Key Responsibilities: Databricks & Data Platform Architecture Lead the architecture and evolution of Takeda's enterprise data platform using the Databricks Data Intelligence Platform. Design scalable Lakehouse architectures supporting structured, semi-structured, and unstructured data. Establish enterprise architecture standards leveraging Unity Catalog, Lakeflow, Mosaic AI, Delta Sharing, and Data Intelligence capabilities. Define reusable patterns for Data Products, Data Mesh, governance, metadata management, and AI-ready data assets. Drive platform modernization to enable real-time analytics, AI, and self-service data consumption. Agentic AI Architecture Architect enterprise Agentic AI solutions that enable autonomous agents, AI copilots, and intelligent workflow automation. Design reusable frameworks for multi-agent orchestration, Retrieval-Augmented Generation (RAG), enterprise knowledge systems, and AI-assisted decision intelligence. Define architecture patterns for reasoning, planning, memory, tool integration, and human-in-the-loop workflows. Enable secure integration of AI agents with enterprise applications, APIs, and data platforms. Cloud Architecture (AWS) Design cloud-native architectures on AWS that provide scalable, secure, resilient, and cost-effective foundations for enterprise data and AI workloads. Define architectural standards for data integration, storage, compute, security, networking, and observability aligned with enterprise cloud best practices. Partner with cloud engineering teams to optimize platform scalability, performance, governance, and operational excellence. Enterprise AI Enablement Design enterprise architectures supporting LLMs, vector search, semantic retrieval, knowledge graphs, and AI-powered applications. Develop reusable architecture blueprints and accelerators that simplify adoption of Generative AI and Agentic AI across business domains. Ensure AI solutions are scalable, governed, observable, and aligned with enterprise architecture standards. Governance & Technology Leadership Define enterprise standards for Data Governance, AI Governance, Responsible AI, security, privacy, and compliance. Lead architecture reviews, technology evaluations, and adoption of emerging Databricks, AWS, and Agentic AI capabilities. Mentor engineering teams and promote reusable architecture patterns, best practices, and technical standards. Required Qualifications Experience 8years to 12 years of experience in Data Architecture, Solution Architecture, or Enterprise Data Engineering. Deep expertise with the Databricks Data Intelligence Platform and modern Lakehouse architecture. Strong experience architecting enterprise cloud solutions on AWS. Experience designing and implementing Generative AI, Agentic AI, and Retrieval-Augmented Generation (RAG) solutions. Proven experience delivering enterprise-scale data and AI platforms from architecture through production deployment. Technical Skills: Databricks Databricks Data Intelligence Platform Lakehouse Architecture Unity Catalog Lakeflow Mosaic AI Delta Sharing Data Products AWS Cloud Architecture Cloud-Native Data Platforms Security & Identity Infrastructure as Code Observability Cost Optimization Agentic AI & Generative AI Large Language Models (LLMs) Agentic AI Multi-Agent Systems Retrieval-Augmented Generation (RAG) AI Copilots AI Orchestration Prompt Engineering Vector Search Knowledge Graphs LLMOps Enterprise Architecture Enterprise Data Architecture Data Mesh Data Governance Metadata Management Solution Architecture API & Event-Driven Integration Responsible AI AI Governance Stakeholder Engagement Act as the enterprise champion for Agentic AI adoption. Collaborate with: Enterprise Architecture AI & Data Team Business and domain teams Lead change management, enablement, and training for agentic systems. Qualifications Education Bachelor’s in Computer Science, AI/ML, Data Science, or related field Master’s preferred Minimum 8 years of total experience and 3–5 years of relevant experience in AI/ML product management, platform leadership, or advanced analytics Proven experience with: LLMs, RAG, and agentic frameworks Multi-agent systems and workflow automation Enterprise-scale AI product delivery Experience in building end-to-end AI-driven products (from concept to production) Skills & Competencies Strong product leadership: strategy, roadmap, prioritization, OKRs Deep understanding of: LLM architectures, embeddings, and retrieval Agent design patterns (planner-