Solution Architect – AI, RPA, Data Analytics

About the job

1. Position Summary The Solution Architect – AI, RPA, Data Analytics is responsible for designing end-to-end solutions for specific AI, analytics and automation use cases. The role converts business requirements into detailed technical designs, ensuring alignment with the enterprise architecture defined by the Architecture Lead and enabling implementation by Data Engineering and Development teams. The focus is on project and product level architecture for initiatives such as Manufacturing Control Tower, Golden Batch, Yield Improvement, OOS prediction, AI-based pricing and advanced dashboards. 2. Roles Played • Lead Solution Architect for AI/analytics/RPA projects. • Bridge between business requirements and technical implementation. • Owner of solution level design, data flows, integration and non-functional requirements. • Technical design authority for project teams, guiding Data Engineers and Developers. 3. Key Platforms / Applications in Scope • Azure Data Platform, data lake, data warehouse and BI tools. • AI/ML and MLOps platforms. • RPA platforms (UiPath, Microsoft Power Automate). • SAP, MES/eBMR, LIMS, Empower and planning & scheduling tools. • Integration components – APIs, MQTT Broker, Enterprise Service Bus (ESB), event streaming and ETL tools 4. Overall Job Responsibilities: A. Solution Design & Blueprinting (≈ 25%) • Lead solution architecture for assigned AI/analytics/RPA projects. • Translate functional and business requirements into end-to-end solution designs, including components, data flows, interfaces and NFRs. • Prepare and own Solution Design Documents (SDD), architecture diagrams and related artefacts. • Ensure designs comply with enterprise architecture standards and technology choices. B. Requirements & Use-Case Analysis (≈ 15%) • Work with business teams, IT Business Analysts and Program Manager to clarify requirements. • Identify technical constraints, dependencies and assumptions for each solution. • Break down complex use cases (e.g., Manufacturing Control Tower, Golden Batch, OOS prediction) into manageable components. C. Technical Leadership & Delivery Support (≈ 20%) • Provide hands on guidance to Data Engineering Lead and Full Stack Developer Lead during implementation. • Support estimation, effort planning and risk identification from architecture perspective. • Participate in code and design reviews for critical components to ensure adherence to the approved solution architecture. D. Data, Integration & Security Design (≈ 20%) • Design data structures, data flows and integration patterns required by the solution. • Define APIs, message formats and data contracts between systems. • Ensure security, access control, data privacy and audit requirements are built into the solution design. E. Quality, Performance & Compliance (≈ 10%) • Define non-functional requirements (performance, scalability, availability, resilience) for the solution. • Collaborate with QA and Validation teams to ensure test scenarios cover architecture aspects. • Ensure designs adhere to GxP, CSV, data integrity and cybersecurity expectations where applicable. F. Documentation & Knowledge Sharing (≈ 10%) • Maintain solution architecture artefacts in a structured repository. • Conduct knowledge-transfer sessions to project and support teams. • Capture reusable patterns and lessons learned for future projects. 5. External Interfaces • Implementation partners and system integrators. • Platform vendors (cloud, AI/ML, RPA, integration) for solution-level guidance. 6. Internal Interfaces • Architecture Lead – AI, RPA, Data Science & Data Analytics. • Program Manager and IT PMO. • Data Engineering Lead, Full Stack Developer Lead and RPA developers. • Business users and process owners across Manufacturing, Quality, Supply Chain, Commercial and Finance. 8. Education • Bachelor’s degree in engineering / computer science / IT – mandatory. • Master’s degree in IT / Data / AI / MBA – preferred. 9. Experience • 12–18 years of IT experience, with strong background in solution design and architecture. • Minimum 4–6 years as Solution Architect or Senior Technical Lead on data/analytics/AI/RPA projects. • Experience integrating with SAP/MES/LIMS or similar enterprise systems preferred. • Exposure to regulated pharma / life sciences environments is desirable. 10. Knowledge & Skills (Functional / Technical) • Solution architecture techniques and documentation standards. • Strong understanding of data warehousing, data lakes, BI and analytics stack. • Working knowledge of AI/ML toolchains and how they integrate with data platforms. • Integration technologies (APIs, ESB, ETL, streaming). • Understanding of RPA concepts and how bots interact with applications. • Awareness of GxP, CSV, data integrity and basic cybersecurity principles. 11. Leadership / Managerial Attributes • Strong analytical and problem-solving ability. • Able to lead technical discussions and drive decisions in cross-functional teams. • Good communication skills with both IT and business stakeholders. • Collaborative mindset and ownership attitude. 12. Other Requirements • Architecture or cloud certifications (e.g., Azure Architect) are an advantage. • Comfortable working in agile/hybrid project models.

Requirements

• Solution Architecture
• Data Engineering
• Stakeholder Management
• AI/ML
• RPA

Qualifications

• Bachelor’s degree in engineering / computer science / IT
• Master’s degree in IT / Data / AI / MBA

Preferred Technologies

• Solution Architecture
• Data Engineering
• Stakeholder Management
• AI/ML
• RPA

About the company

Glenmark Pharmaceuticals is a global pharmaceutical company focused on innovation, product development, and high-quality healthcare solutions.