Architecture Lead – AI, RPA, Data Science & Data Analytics

About the job

• Position Summary The Architecture Lead – AI, RPA, Data Science & Data Analytics is responsible for the global IT architecture that underpins Glenmark’s AI, data and automation initiatives. The role defines and governs the target state enterprise architecture and reference models for AI/ML, data platforms, analytics, RPA and integrations, ensuring that solutions such as Manufacturing Control Tower, Golden Batch and Yield Optimization, OOS Predictors and AI-based US Pricing and EU Tender Analytics are built on a scalable, secure and compliant foundation. This is a primarily architecture and governance role, working closely with the Program Manager, Solution Architect, Data Engineering Lead and Full Stack Lead, rather than managing day-to-day project delivery. 2. Roles Played • Enterprise Architect for AI, data, analytics and automation capabilities across Glenmark. • Owner of AI/data/automation target architecture and roadmap. • Architecture authority for major AI initiatives and platforms. • Key member of the Architecture Review Team for AI and analytics solutions. • Technology advisor to CIO / IT leadership and Business Heads for AI and automation-related architecture decisions. 3. Key Platforms / Applications in Scope • SAP ECC or S/4HANA, MES / eBMR, LIMS, Empower, Plant Automation and L2 systems. • Azure Data Platform, data lake, data warehouse, and BI tools such as Power BI and Tableau. • AI/ML and ML Ops platforms (e.g., Azure ML, Databricks). • RPA platforms (UiPath, Microsoft Power Automate) and Intelligent Automation components. • API gateway, MQTT Broker, Enterprise Service Bus (ESB)/middleware, event streaming and ETL tools. • Security, identity and monitoring platforms (e.g., ELK, SIEM). 4. Overall Job Responsibilities: A. Strategy & Enterprise Architecture Roadmap (≈ 20%) • Define target state architecture for AI, data, analytics and automation across Glenmark. • Build and maintain a multi year architecture roadmap aligned to Glenmark’s digital strategy. • Translate business priorities (Control Tower, Golden Batch, OOS, AI Pricing, EU Tenders) into architecture principles and patterns. • Review and update architecture based on new technology, business needs and regulatory changes. B. AI, Data & Analytics Architecture (≈ 20%) • Design the overall data and analytics architecture, including data lake, data warehouse, semantic layers and master data domains. • Define reference architectures for AI/ML platforms, ML Ops, model registry and deployment patterns. • Ensure architecture supports predictive and prescriptive use cases across Manufacturing, Quality, Supply Chain, Commercial and Finance. • Review and approve AI/analytics solution designs proposed by the Solution Architect and project teams. C. Automation & Application Architecture (≈ 15%) • Define reference architecture for RPA and Intelligent Automation, including integration with core systems and AI services. • Publish design patterns for reusable components, microservices, APIs and orchestration workflows. • Ensure applications and automation solutions can scale globally and meet security and compliance requirements. D. Integration, Cloud & Infrastructure Architecture (≈ 20%) • Define patterns for integration (APIs, MQTT broker, ESB, events, ETL/ELT) across AI/data and transactional systems. • Collaborate with infrastructure and security team on Azure cloud architecture for AI/data workloads. • Ensure non-functional requirements (availability, performance, DR, security) are built into all architecture designs. E. Architecture Governance, Standards & Compliance (≈ 15%) • Establish and run an Architecture Review Team for AI/data/automation solutions. • Maintain architecture principles, standards, patterns and reusable assets. • Ensure all AI and data architecture meet GxP, CSV, data integrity, cybersecurity and Data privacy requirements. • Maintain an architecture repository with documentation of current and target state architectures. F. Stakeholder Management & Capability Building (≈ 10%) • Partner with Business Heads and IT leaders to influence technology choices aligned with architecture. • Mentor Solution Architects, Data Engineers and Developers on architecture best practices. • Conduct workshops and knowledge sessions to socialize new architectures and patterns globally. 5. External Interfaces • Cloud and platform providers (Azure, analytics and AI vendors). • System integrators and consulting partners. • RPA, AI/ML and integration technology vendors. • External auditors and validation partners (architecture and design aspects). 6. Internal Interfaces • CIO / VP – IT and IT leadership team. • Program Manager – AI, RPA, Data Science & Data Analytics. • Solution Architect, Data Engineering Lead and Full Stack Developer Lead. • Functional heads for Manufacturing, Quality, Supply Chain, R&D, Commercial, Finance and HR. • Infrastructure, Security and Operations teams. 8. Education • Bachelor’s degree in engineering / computer science / IT – mandatory. • Master’s degree (MBA / M.Tech / MS in Data / AI / Information Systems) – preferred. • Enterprise architecture certification (TOGAF or equivalent) – added advantage. 9. Experience • 15–20 years of IT experience with significant architecture responsibility. • At least 5–8 years as Enterprise / Solution Architect in large/global organizations. • Preferably experience in pharmaceutical / life sciences / regulated industries. • Proven experience defining architecture for data platforms, AI/ML and automation at scale. 10. Knowledge & Skills (Functional / Technical) • Enterprise and solution architecture methods, modelling tools and techniques. • Strong conceptual understanding of AI/ML, data engineering, analytics and ML Ops. • Integration architecture: APIs, microservices, MQTT broker, ESB and event-driven systems. • Cloud architecture (preferably Azure) for data/AI workloads. • Awareness of GxP, CSV, data integrity, cybersecurity and privacy impacts on architecture. 11. Leadership / Managerial Attributes • Strategic thinker with strong problem solving skills. • High influencing ability without direct line authority. • Excellent communication with both technical and business stakeholders. • Collaborative and able to drive consensus in a matrix environment. 12. Other Requirements • Familiarity with ITIL, project portfolio governance and program delivery methods. • Willingness to travel to Glenmark sites globally for architecture workshops and reviews.

Requirements

• Enterprise Architecture
• AI
• Data Analytics
• Data Engineering
• RPA

Qualifications

• Bachelor’s degree in engineering/computer science/IT
• Master’s degree (MBA/M.Tech/MS in Data/AI/Information Systems)
• Enterprise architecture certification (TOGAF or equivalent)

Preferred Technologies

• Enterprise Architecture
• AI
• Data Analytics
• Data Engineering
• RPA

About the company

Glenmark Pharmaceuticals is a leading global pharmaceutical company that is committed to core philosophies of innovation and new therapies. With an expansive pharmaceutical portfolio, the company focuses on delivering high-quality healthcare solutions.