Captain -AI Foundation Platform POD

Role: Captain -AI Foundation Platform POD

Astellas’ Global Capability Centers – Overview

Astellas’ Global Capability Centers (GCCs) are strategically located sites that give Astellas the ability to access talent across various functions in the value chain and to co-locate core capabilities that are currently dispersed. Our three GCCs are in India, Poland and Mexico.
The GCCs will enhance our operational efficiency, resilience and innovation potential, enabling a timely response to changing business demands. Our GCCs are an integral part of Astellas, guided by our shared values and behaviors, and are critical enablers of the company’s strategic priorities, sustainable growth, and commitment to turn innovative science into VALUE for patients. This position is based in Bengaluru and will require 1-2 days onsite/week

Purpose and Scope

The AI Foundation Platform POD Captain leads the enterprise AI platform strategy and execution at Astellas, driving innovation and operational excellence across Microsoft Azure, GCP, Databricks, and Microsoft Copilot. This role is accountable for architecting, designing, developing, and operating a scalable, secure, and future-ready AI platform that empowers data science, machine learning, and advanced analytics initiatives. The captain will coordinate cross-functional teams, foster a culture of experimentation and ownership, and ensure seamless integration of AI capabilities within the broader enterprise architecture to accelerate business value from AI

Responsibilities and Accountabilities:

The AI Foundation Platform POD Captain is a strategic and technical leader accountable for the successful implementation, scaling, and continuous evolution of Astellas' enterprise AI platform. The role owns the platform vision and roadmap, ensuring scalable, secure and and compliant AI enhancement across the enterprise. within a modern operating model. This role blends hands-on technical expertise with enterprise leadership to drive business value through AI productization, platform enablement, and cross-functional collaboration.

Strategic Leadership & Vision (Accountability: AI Platform & Strategy)

• Own and champion the vision for the AI foundation platform, ensuring alignment with Astellas' overall digital and business strategy.
• Develop and execute the long-term roadmap supporting diverse AI/ML workloads, data science experimentation, and operationalization.
• Evaluate, select and integrate emerging AI technologies and platform capabilities e.g. Microsoft Copilot, generative AI, and MLOps frameworks, with clear value hypotheses.
• Act as the senior subject matter expert and evangelist for AI platform best practices, educating stakeholders and promoting an AI-driven culture.

Platform & Enablement (Accountability: Reliable & Scalable AI Platform)

• Oversee the architecture, design principles, and operations of the enterprise AI platform, ensuring scalability, security, performance, and cost-efficiency.
• Provide tooling, best practices, guidelines, and self-service capabilities to empower domain teams to independently build, deploy, and manage AI solutions.
• Drive the implementation of Infrastructure-as-Code (IaC) and automation for platform resources, enabling consistent and efficient provisioning across environments.
• Define and monitor platform KPIs (availability, performance, cost efficiency, adoption) and continuously optimize platform health.

AI Product Lifecycle & Marketplace (Accountability: AI Productization & Value Delivery)

• Collaborate with domain teams to define and enforce enterprise standards for reusable, interoperable AI products and solutions.
• Lead the development and evolution of an enterprise AI marketplace, ensuring discoverability, usability, and consumer-centric access to AI assets.
• Define governance processes across the AI product lifecycle, from ideation and development to deployment, versioning, deprecation, and consumption.
• Promote architectural patterns and interoperability standards that enable cross-domain consumption and scaling of AI capabilities.

Governance & Compliance (Accountability: Trust, Security & Responsible AI)

• Partner with governance teams to embed policies, quality standards, security controls, and compliance requirements directly into the AI platform and product development processes.
• Ensure the platform supports full auditability, lineage, and transparency for all AI products, supporting both regulatory readiness and responsible AI principles.
• Implement and enforce robust security measures, access controls, and data privacy protocols, adhering to industry best practices and regulatory requirements.

Team Leadership & Collaboration (Accountability: High-Performing POD & Cross-Functional Alignment)

• Lead, coach, and develop a high-performing AI Foundation POD, fostering a culture of innovation, collaboration, and continuous improvement.
• Serve as the primary technical integrator across data science, engineering, platform, security, and governance teams to ensure seamless integration and stakeholder alignment.
• Effectively communicate technical concepts and strategic direction to both technical and non-technical stakeholders, influencing decision-making and driving consensus.
• Champion and implement Agile and DevOps (and MLOps) methodologies within the POD and promote their adoption across AI development practices.

Hands-on Technical Expertise (Accountability: Technical Excellence & Problem Solving)

• Maintain deep, hands-on expertise with AI platform features and services (e.g., model lifecycle management, orchestration, monitoring, and integration with Copilot).
• Provide technical direction on AI/ML architecture, data/model pipelines, and deployment patterns suitable for enterprise-scale AI.
• Lead critical problem resolution for high-impact or high-risk AI workloads.
• Drive rapid experimentation and lead the development of proofs-of-concept and prototypes for new AI capabilities or architectural patterns.

Required Qualifications:

Education & Core Experience

• Bachelor’s degree in computer science, Engineering, Information Systems, or a closely related quantitative field.
• Minimum of 10 years of progressive experience in AI/ML engineering, platform architecture, or technical leadership within a large-scale enterprise environment.
• Minimum of 3+ years of direct, hands-on experience leading the design, implementation, and operationalization of enterprise AI platforms.
• Proven experience in building, deploying, and operating AI/ML solutions on at least one major cloud provider (e.g., Microsoft Azure, Google Cloud Platform, or Databricks).

Technical Expertise

• Deep and practical understanding of AI/ML platform architectural patterns, including model lifecycle management, MLOps, and responsible AI.
• Mastery of data and model pipeline design, orchestration, and monitoring.
• Expertise in designing, building, and optimizing robust, scalable, and fault-tolerant AI/ML pipelines using cloud-native services.
• Strong proficiency in at least one relevant programming language for AI/ML (e.g., Python, R, Scala).
• Demonstrable experience with Infrastructure-as-Code (IaC) tools (e.g., Terraform, ARM templates, Cloud Deployment Manager) for automating deployment and management of platform resources.
• Solid understanding of AI governance concepts (e.g., model explainability, bias detection, auditability) and their implementation within an enterprise context.
• Comprehensive knowledge of data and model security best practices for cloud AI platforms, including access control, encryption, and privacy regulations.

Leadership & Strategic Acumen

• Proven experience in leading, mentoring, and developing high-performing technical teams (e.g., AI/ML engineers, platform engineers).
• Exceptional verbal and written communication skills, with the ability to clearly articulate complex technical concepts, strategic visions, and business value to diverse audiences.
• Demonstrated ability to drive organizational change and influence key stakeholders without direct authority.
• Strong analytical, problem-solving, and critical thinking abilities, with a track record of successfully resolving complex technical challenges.
• Experience in facilitating cross-functional collaboration between data science, engineering, governance, and business units.

Preferred Qualifications :

• Master’s degree or higher in Computer Science, Data Science, Engineering, or a related field.
• Certifications in AI/ML, cloud platforms, or MLOps (e.g., Azure AI Engineer, Google Professional ML Engineer, Databricks certifications).
• Experience with AI governance and cataloging tools (e.g., Azure Purview, Google Data Catalog, Databricks Unity Catalog).
• Familiarity with DevOps and MLOps practices, including CI/CD pipelines for AI/ML products and model deployment.
• Experience with real-time data processing and streaming technologies.
• Knowledge of data visualization and AI product consumption tools (e.g., Power BI, Tableau, Looker).
• Prior experience in the pharmaceutical or life sciences industry, understanding relevant data privacy and regulatory compliance (e.g., GxP, HIPAA, GDPR).
• Experience with cost optimization strategies for large-scale cloud AI platforms.
• Active participation in the AI/ML or platform engineering community (e.g., speaking engagements, open-source contributions, blog posts).