New opportunity

Associate Director, Data Analytics & AI - Solution Architect

MSD · Hyderabad, India

About this role

The Associate Director, Data Analytics & AI - Solution Architect will lead the development of forward-looking architecture to power enterprise-scale data and AI initiatives. This role focuses on designing scalable solutions across Data & Analytics (DnA), Intelligent Automation (IA), and AI domains to drive insight-driven decision-making within a global healthcare biopharma environment. Responsibilities include analyzing current architectures to identify gaps, defining target-state architectures, and ensuring seamless integration and compliance. The architect will monitor industry trends to shape roadmaps, advocate for user-centric design, manage technical debt, and lead agile delivery of reusable technology products. Collaboration with cross-functional teams, including developers and product owners, is essential to drive innovation and operational excellence through DevSecOps and platform automation. Required qualifications include a Bachelor’s degree in Computer Science or a related field and 10+ years of experience in solution architecture, systems integration, and end-to-end system design. Candidates must possess deep knowledge of data platforms, AI technologies, Data Lakehouse architecture, and enterprise-scale SDLC. Preferred qualifications include advanced certifications such as TOGAF, experience in regulated industries like Life Sciences, and proficiency in GenAI architecture and LLMOps. This is a hybrid role based in Hyderabad.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Artificial IntelligenceData AnalyticsData ModelingDesign ApplicationsGenerative AIRelease ManagementRequirements ManagementSolution ArchitectureSystem DesignsSystem IntegrationDatabase ModelingCloud-native platformsCI/CD pipelinesObservabilityInfrastructure as CodeMachine learning lifecycleIntelligent automationOrchestration frameworks

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Enterprise-Scale Architecture Design
The role requires designing scalable, forward-looking architectures across Data & Analytics, Intelligent Automation, and AI domains. You must demonstrate the ability to bridge current-state gaps while defining a target-state that supports global, enterprise-level operations.
Regulatory Compliance and Governance
Operating within a biopharma environment necessitates strict adherence to GxP, SOX, and data privacy regulations. You must integrate these compliance requirements directly into your architectural blueprints and governance frameworks.
Agile Delivery and Technical Debt Management
You are expected to lead the agile delivery of reusable technology products while actively managing and remediating technical debt. This requires balancing rapid innovation with the long-term stability and maintainability of the enterprise platform.
Cross-Functional Stakeholder Influence
Success depends on your ability to communicate complex architectural trade-offs to diverse stakeholders. You must build strong networks across IT and business units to drive alignment on technology roadmaps and user-centric design principles.

Likely challenges to think through

Balancing Innovation with Regulated Constraints
Implementing cutting-edge AI and GenAI solutions in a highly regulated life sciences environment creates a tension between agility and compliance. Prepare by discussing how you have successfully navigated GxP or similar regulatory frameworks while deploying modern cloud-native or AI-driven architectures.
Managing Legacy Integration and Technical Debt
The requirement to analyze current architectures and manage technical debt suggests a complex, heterogeneous environment. Prepare to explain your methodology for identifying architectural bottlenecks and your strategy for modernizing legacy systems without disrupting critical business operations.
Driving Adoption of Emerging AI Capabilities
As an architect, you must guide the adoption of GenAI and LLMOps across the organization. The challenge lies in moving from theoretical architecture to practical, scalable implementation. Prepare by articulating how you define reference architectures that are both technically sound and consumable by engineering teams.

Practice your answers

Technical practice

Designing Data Lakehouse Architectures

What to coverExplain the core components of a Data Lakehouse architecture and how it addresses the limitations of traditional data warehouses and data lakes. Discuss how you would ensure data quality, lineage, and cataloging within this framework to support enterprise-scale analytics.

Implementing GenAI and LLMOps

What to coverDescribe the reference architecture for deploying Generative AI solutions in a regulated environment. Address how you manage the machine learning lifecycle, specifically focusing on LLMOps practices like model monitoring, versioning, and ensuring output reliability.

Managing Technical Debt in Enterprise Systems

What to coverHow do you identify and prioritize technical debt within a complex, global IT ecosystem? Provide a strategy for balancing the need for rapid innovation with the necessity of maintaining long-term architectural health and system stability.

DevSecOps and Compliance in Regulated Industries

What to coverGiven the requirement for GxP and SOX compliance, how do you integrate security and compliance checks into CI/CD pipelines? Explain how you maintain auditability while enabling agile delivery and platform automation.

Evaluating Architectural Trade-offs

What to coverWhen presented with multiple architectural alternatives for a data platform, what criteria do you use to evaluate their financial impact and technical feasibility? Discuss how you communicate these trade-offs to stakeholders with varying levels of technical expertise.

Intelligent Automation Frameworks

What to coverDiscuss your experience in designing solutions that leverage intelligent automation tools. How do you ensure these automated processes are scalable, maintainable, and integrated seamlessly with existing enterprise applications?

Cloud-Native Platform Strategy

What to coverWhat are the key considerations when designing for cloud-native platforms (e.g., AWS or Azure) in a global biopharma context? Address aspects such as infrastructure as code, observability, and ensuring high availability across regions.

Systems Integration and Data Governance

What to coverDescribe your approach to integrating disparate data sources into a unified enterprise architecture. How do you enforce governance practices while promoting self-service analytics for business users?

Behavioural practice

Influencing Cross-Functional Stakeholders

What to coverDescribe a situation where you had to gain alignment from diverse stakeholders on a complex architectural decision. How did you articulate the rationale and manage conflicting priorities to reach a consensus?

Leading Through Digital Transformation

What to coverProvide an example of a time you led a team through a significant shift in technology or architectural strategy. What challenges did you face, and how did you ensure the team remained focused on delivering business value?

Navigating Regulatory Constraints

What to coverTell me about a time you had to design a solution that required strict adherence to data privacy or industry-specific regulations. How did you balance the need for innovation with the constraints imposed by compliance requirements?

Fostering Innovation in a Large Organization

What to coverDescribe a time you identified an emerging technology trend and successfully advocated for its adoption within your organization. What steps did you take to prove its value and integrate it into the existing roadmap?

Sharpen your knowledge

Topics to prepare

Data Lakehouse Architecture
Review the conceptual differences between traditional data warehouses and data lakes, and how the Lakehouse paradigm unifies them. Exercise: Sketch a high-level architecture diagram for a biopharma data platform that supports both structured clinical trial data and unstructured research data, ensuring compliance with GxP standards.
Regulated Industry Compliance (GxP/SOX)
Understand the impact of GxP and SOX regulations on IT systems in life sciences. Exercise: Prepare a brief explanation of how you would design an automated audit trail for an AI-driven decision-support tool to ensure data integrity and traceability in a regulated environment.
LLMOps and GenAI Lifecycle
Focus on the operationalization of Large Language Models, including deployment, monitoring, and fine-tuning. Exercise: Outline a strategy for managing LLM drift and performance monitoring in a production environment, specifically addressing how to maintain model accuracy over time.

Skills to revise

Solution Architecture Design
Practice translating high-level business requirements into technical blueprints. Exercise: Create a document that evaluates two architectural alternatives for an enterprise-scale data platform, comparing them based on cost, scalability, and integration complexity.
DevSecOps and CI/CD Pipeline Automation
Focus on integrating security and automated testing into the software delivery lifecycle. Exercise: Describe how you would implement a CI/CD pipeline for an AI model that includes automated security scanning and compliance checks before deployment to production.
Technical Debt Management
Develop a framework for identifying and prioritizing the remediation of technical debt. Exercise: Create a template for a 'Technical Debt Register' that categorizes architectural issues by business impact and effort, and propose a roadmap for addressing them alongside new feature development.

Your preparation checklist

Map your experience to regulated industry standards
Prepare to discuss your experience with GxP, SOX, and data privacy regulations. Create a summary of how you have previously balanced architectural innovation with strict compliance requirements in a life sciences or similar regulated environment.
Develop a Data Lakehouse and AI reference architecture
Draft a high-level architectural diagram or whitepaper demonstrating your approach to integrating Data Lakehouse patterns with AI/ML lifecycles. Be ready to explain how you handle data quality, lineage, and cataloging at enterprise scale.
Prepare a technical debt management strategy
Outline a framework for identifying, prioritizing, and remediating technical debt within a large-scale data ecosystem. Focus on how you balance the need for rapid agile delivery with the long-term sustainability of reusable technology products.
Formulate your GenAI and LLMOps perspective
Articulate a clear vision for implementing GenAI within an enterprise. Prepare to discuss specific reference architectures for LLMOps, including how you manage model deployment, observability, and cost-efficiency in a production environment.
Draft strategic questions for the interviewers
Prepare thoughtful questions regarding the current maturity of MSD's data platforms, the primary challenges in their digital transformation journey, and how the architecture team collaborates with product owners to define the roadmap for intelligent automation.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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