Principal Data & AI Platforms Architect

About the position

GSK is one of the world's foremost pharmaceutical and healthcare companies, and we are proud to be part of an industry that improves the lives of others. We embark on a significant transformation journey to support GSK in becoming a top-quartile data-enabled organization. This is an exciting time to join GSK. We are embracing new data technologies to improve the development, manufacture, and distribution of GSK's vital products to patients and consumers worldwide. You will be part of a team building a robust data and analytics ecosystem, allowing GSK to drive higher value by placing data at the core of its strategic and operational decisions. Principal Data & AI Platforms Architect is responsible for overseeing architecture, strategy and operations for the Analytics & AI teams.

Responsibilities

• Lead the transformation of data operations by embedding AI-driven automation, reducing manual intervention, and enhancing operational efficiency.
• Develop the technical vision for data and AI platforms, including infrastructure, tools, and processes for agentic AI systems.
• Evaluate and recommend appropriate technologies for data management, analytics, machine learning operations, and AI model deployment.
• Drive standardization and reuse by identifying and socializing best-in-class technologies and methodologies and avoid technology sprawl by leading subject area reviews in Architecture Review Board (ARB) meetings.
• Demonstrate a strong focus on innovation by constantly identifying and experimenting with data technologies that push the boundaries of what's possible, ensuring the organization remains at the forefront of digital transformation.
• Champion the development of frictionless, Data and AI platforms and ecosystems.
• Build and maintain strong relationships with stakeholders across business units to ensure successful adoption and optimization of data products and cloud services.
• Monitor industry trends, emerging technologies, and best practices to drive the culture of innovation and continuous improvement.
• Lead, mentor, and develop a high-performing team of data professionals, and cloud specialists.
• Foster a culture of collaboration, accountability, and excellence within the team, driving continuous learning and improvement.

Requirements

• Bachelor's degree in computer science, electrical engineering, electronics engineering, mathematics, statistics, or information systems.
• 8+ years of professional experience.
• Experience in cloud technologies: AWS (Redshift, S3, RDS, DynamoDB, Neptune), or Azure (Synapse, Data Lake, Databricks, CosmosDB), or GCP (BigQuery, Bigtable, CloudSQL, Spanner, Firebase).
• Experience with data technologies: SQL, NoSQL, Data Warehousing, Data Lakes, and Data Lakehouse architectures with knowledge of Big Data technologies: Hadoop ecosystem, Spark or Kafka.
• Programming skills in Python or Java, with experience in data processing libraries.
• Experience designing and implementing big data solutions and AI/ML platforms such as MLflow, Kubeflow, SageMaker, or Azure ML.
• Experience with machine learning operations (MLOps) and AI model deployment.
• Experience with Agentic AI systems and platforms, including Agent Orchestration frameworks.

Nice-to-haves

• Strong strategic, analytical, and problem-solving skills, with the ability to make data-driven decisions.
• Exceptional communication skills, with the ability to collaborate with both technical and non-technical stakeholders.
• Certifications in relevant cloud platforms, data technologies and AI platforms & frameworks.
• Experience with Infrastructure as Code: Terraform, CloudFormation, or ARM templates.
• Experience with knowledge graph technologies, ontology design, and semantic modelling with experience with graph databases (e.g., Neo4j, Neptune, etc.) and query languages (e.g., Cypher, SPARQL).
• Experience with containerization (Docker, Kubernetes) and infrastructure as code.
• Experience designing and implementing autonomous agent systems and multi-agent architectures.
• Hands on experience with retrieval-augmented generation (RAG) systems and vector databases.
• Experience with agent frameworks such as LangChain, AutoGPT, or custom agent implementations.
• Familiarity with large language model (LLM) orchestration and optimization.
• Knowledge of real-time data processing frameworks, data mesh architecture and domain-driven design.
• Experience with prompt engineering and AI agent behaviour design.