Forward Deployed Engineer – AWS Stack

Expected Annual Base Salary Range for role: €63,800 to €118,400

The base salary offered is determined based on gender-neutral objectives, such as relevant skills, competencies and experience in accordance with the Novartis pay setting policy and upon joining Novartis will be reviewed periodically.

In addition to your base salary, you may be eligible for a performance-based bonus depending on certain performance parameters.

The rewards of being part of our team go far beyond base pay and incentives. We also offer a variety of competitive benefits in kind to help you thrive personally and professionally, such as insurance plans, retirement plans, wellbeing resources and global recognition programs. In addition, we provide flexible and hybrid working options, where possible, and minimum 14 weeks paid parental leave.

Pay equity is a fundamental principle of our employment policy and reflects our commitment to create a diverse, equitable and inclusive environment that treats all employees with dignity and respect, as outlined in our Code of Ethics.

Benefits and compensation may vary by country and are subject to local legal requirements, including provisions of collective bargaining agreements where applicable. A full overview of your compensation package, including any relevant collective bargaining agreement details applicable to your role based on your employment location and Novartis employer entity, will be communicated separately to you during the application process.

Salary Range: €63,800.00 - €118,400.00

Location: Barcelona, Spain

At Novartis, we combine cutting-edge science with innovative digital technology to help bring medicines to patients faster.

As a Forward Deployed Engineer - AWS Stack, you will work at the intersection of AI, engineering, product and business, turning high-value challenges into secure, scalable and production-ready solutions on AWS.

You will build enterprise GenAI and agentic AI solutions using technologies such as Amazon Bedrock, Amazon SageMaker and AWS cloud services, helping teams move from high-value business problems to production-ready AI-enabled products.

This is a hands-on role where you will move from discovery and rapid prototyping through to engineering, deployment, adoption and continuous improvement.

• Own the end-to-end delivery of AWS-based AI and agentic AI solutions, translating business priorities into clear technical solutions and measurable outcomes.
• Work directly with Product Managers, Product Owners and business stakeholders to understand workflows, rapidly prototype solutions and take viable concepts into production.
• Design, build and deploy enterprise GenAI and agentic AI solutions using AWS cloud services, including technologies such as Amazon Bedrock, Amazon SageMaker, OpenSearch, Lambda, API Gateway, S3 and DynamoDB.
• Design agent intelligence using prompts, reasoning, retrieval, tool calls, integrations and appropriate guardrails to deliver accurate and responsible outcomes.
• 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.

Artificial Intelligence (AI), Biostatistics, Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Generative AI, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Neuro-Linguistic Programming (NLP), Organizational Awareness, Pandas Python Library, Python (Programming Language), R Programming, Stakeholder Engagement, Statistical Analysis, Structured Query Language (SQL), Time Series Analysis