Build secure integrations across AI capabilities, enterprise data and business platforms, including AWS services, MuleSoft, Veeva and other approved enterprise technologies as relevant.
Apply strong engineering practices across architecture, coding, testing, CI/CD, security, privacy, observability, performance and responsible AI.
Evaluate AWS technologies and engineering approaches, making pragmatic decisions around scalability, maintainability, security, performance and cost.
Turn ambiguous business requirements into practical, production-ready AWS solutions through rapid prototyping and iterative delivery.
Create reusable AWS components, patterns and documentation, sharing knowledge and coaching colleagues to strengthen internal engineering capability.
Measure solution performance, adoption and business value, using insights to continuously improve solutions and inform product and platform roadmaps.
Essential requirements
Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Information Technology or a related discipline, or equivalent professional experience.
Significant hands-on software engineering, AI engineering or technical delivery experience, with a strong track record of taking solutions from problem definition and architecture through to production.
Experience designing or delivering AI-enabled applications using LLMs, GenAI, RAG, prompt engineering, tool calling or agentic workflow patterns.
Strong programming experience in Python and/or Java.
Hands-on experience with relevant AWS AI and cloud technologies such as Amazon Bedrock, SageMaker, OpenSearch, Lambda, API Gateway, S3 and DynamoDB.
Experience designing, building and operating secure, scalable cloud-native solutions on AWS.
Experience integrating enterprise systems and data using APIs, middleware, event-driven approaches or data pipelines, alongside strong production engineering practices.
Strong stakeholder-facing delivery skills, with the ability to translate ambiguous business challenges into clear technical approaches within a complex, regulated enterprise.
Desirable requirements
AWS certifications or practical experience across relevant AWS cloud, AI, machine learning, data and architecture domains would be an advantage.
Experience with complementary enterprise AI and data technologies such as Snowflake Cortex AI, Azure AI, Claude, MuleSoft or Veeva would be beneficial.
Experience delivering AI, GenAI or cloud-native solutions in regulated enterprise environments.
