platform engineer for enterprise AI platforms

AstraZeneca develops life-changing medicines and uses data, analytics, AI, and modern engineering to support research, patient safety, and innovation across its global enterprise. Its AI Platforms and Technologies Team builds and maintains cloud-native infrastructure, vendor platforms, and enterprise AI and data science platforms., * Design, implement, and manage AWS cloud infrastructure using Infrastructure as Code tools such as Terraform or AWS CloudFormation;

• Ensure platform reliability, scalability, and high availability across development, staging, and production environments;
• Design secure, compliant, and multi-tenant infrastructure aligned with enterprise security and governance standards;
• Evaluate, integrate, and optimize Domino, Databricks, Dataiku, and other vendor platforms for enterprise-scale use;
• Assess vendor features, develop proof-of-concepts, and evaluate business value, technical feasibility, and integration impact;
• Plan and execute approved vendor feature implementations with minimal production disruption;
• Serve as the primary technical liaison with vendor partners, managing escalations, roadmap discussions, implementation support, and feature alignment;
• Advise on adoption of current vendor innovations and capabilities;
• Design and implement integrations between AI platforms and enterprise data systems, including AWS S3, Snowflake, and other data warehouses;
• Integrate data governance and access control tools such as Immuta and Collibra with AI platforms;
• Build data pipelines and connectors while maintaining data lineage, quality, and security standards;
• Ensure seamless operation of the platform ecosystem for data scientists and engineers;
• Automate deployment activities using GitHub Actions, AWS CodePipeline, Jenkins, and ArgoCD;
• Develop and maintain pipelines for platform updates, vendor upgrades, and application deployments;
• Automate operational tasks, environment provisioning, configuration management, and infrastructure scaling using Python, Bash, or PowerShell;
• Implement automated testing, validation, and rollback strategies;
• Own assigned platform work packages from planning and design through implementation and deployment;
• Plan, estimate, and schedule work while identifying dependencies across platform components, vendors, and enterprise initiatives;
• Manage dependencies and coordinate across vendor partners, internal teams, and stakeholders;
• Communicate progress, risks, and blockers to the manager and stakeholders;
• Enable and maintain machine learning environments for scalable model training, hosting, and pipelines;
• Implement and manage observability tools such as Amazon CloudWatch, Prometheus/Grafana, and ELK;
• Support container orchestration environments using EKS, Kubernetes, ECS, or Fargate;
• Collaborate with security and compliance teams on IAM, encryption, logging, monitoring, and cost optimization;
• Ensure platform configurations and vendor integrations comply with security, data governance, and regulatory standards;
• Manage and publish curated infrastructure templates through AWS Service Catalogue and platform portals;
• Engage with AI scientists and engineers to understand their needs and translate them into platform capabilities;
• Provide technical guidance and support to platform users and gather feedback for roadmap improvements;
• Contribute to incident response, post-mortems, and continuous improvement of platform operations., * Continuous learning opportunities through hackathons, external partnerships, and professional development;
• Work with AI, cloud, vendor, and open-source technologies;
• Global, multidisciplinary engineering team and investment in digital transformation;
• In-person working averages a minimum of three days per week from the office;
• AstraZeneca supports diversity, equality of opportunity, and applicable work authorization and employment eligibility requirements.

• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent professional experience;
• 5+ Years of hands-on experience with AWS cloud services, including compute, storage, networking, IAM, and cost controls;
• Strong experience with Terraform, AWS CDK, or CloudFormation;
• Proficiency in Linux system administration and networking fundamentals, including VPC design, security groups, and load balancing;
• Solid understanding of IAM policies, encryption, and security best practices;
• Experience with Docker and container orchestration using Kubernetes, preferably EKS, or ECS/Fargate;
• Hands-on experience with CI/CD tools and Git version control;
• Experience supporting AI/ML workloads;
• Hands-on experience with serverless technologies;
• Experience with LLM, RAG architectures, vector databases, and generative AI platforms;
• Experience administering or deploying Domino, Databricks, Dataiku, or other vendor platforms at scale;
• Background in Agile and platform-oriented delivery;
• Proficiency in Python, Bash, or PowerShell for automation and scripting;
• Ability to write clean, efficient, and maintainable infrastructure code;
• Experience with monitoring, logging, and alerting systems;
• Experience integrating data platforms and warehouses such as AWS S3, Snowflake, and Redshift;
• Familiarity with data governance tools such as Immuta and Collibra;
• Understanding of data lineage, data quality, and secure data access patterns;
• Ability to rapidly learn new vendor platforms and tools;
• Strong troubleshooting and problem-solving skills;
• Excellent written and verbal communication skills, including the ability to explain complex technical concepts to diverse audiences;
• Collaborative mindset and ability to work across teams, vendors, and stakeholders;
• Ability to own work packages and drive them to completion with minimal supervision;
• Nice to have: Demonstrated knowledge of Databricks, Domino, Dataiku, or similar ML/AI vendor platforms.