Senior AI Engineer

Senior AI Engineer

Moderna

Warsaw, Poland

This is an opportunity to shape the engineering foundation that powers Moderna's next generation AI capabilities across Supply Chain and Enterprise Digital. As a senior technical leader, you will define the architecture, standards, and deployment patterns that transform AI, machine learning, and Generative AI innovations into secure, scalable, enterprise-grade products. Working across engineering, product, quality, infrastructure, and business teams, you will play a pivotal role in accelerating how AI delivers measurable impact throughout the organization.

Here's What You'll Do

• Provide deep technical leadership for the design, implementation, and operation of AI software platforms and cloud-based deployment patterns supporting Supply Chain and Enterprise Digital;
• Serve as a senior individual contributor, setting technical architecture, building critical software components, establishing engineering standards, and mentoring engineers across multiple regions;
• Own the end-to-end application architecture and delivery strategy for AI and data products, including lakehouse patterns, Unity Catalog governance, CI/CD pipelines, release automation, MLflow model lifecycle management, feature and data product packaging, model serving, vector search, observability, cost and performance optimization, and validated releases supporting GxP use cases;
• Define the technical strategy and reference architecture for AI software delivery, Generative AI applications, agentic workflows, machine learning services, APIs, and cloud-native AI and data platforms;
• Lead cloud deployment standards across development, testing, validation, and production environments, including workspace architecture, Delta Lake and Lakehouse design, Unity Catalog, Workflows and Jobs, MLflow, model serving, deployment automation, observability, and enterprise access governance;
• Design, build, and review critical software components, reusable libraries, orchestration patterns, model-serving interfaces, APIs, and platform accelerators that enable teams to transition AI prototypes into robust production solutions;
• Establish best-in-class engineering practices for AI/ML/LLMOps, including automated testing, continuous integration and continuous delivery, infrastructure as code, release management, model and prompt versioning, evaluation frameworks, monitoring, incident response, and rollback strategies;
• Drive the integration of SAP and enterprise data sources into trusted data products, feature engineering pipelines, semantic layers, and intelligent decision-support services supporting Supply Chain, Manufacturing, Quality, Finance, and Enterprise Digital;
• Partner closely with product owners, enterprise architects, cybersecurity, quality, validation, infrastructure, and business stakeholders to ensure solutions meet enterprise architecture standards, security expectations, regulatory requirements, GxP compliance, and business objectives;
• Coach and mentor engineers by raising the quality of architecture discussions, technical design reviews, and code reviews while helping build a practical AI, Data, and Automation Center of Excellence through reusable engineering standards, frameworks, and accelerators;
• Continuously improve platform reliability, scalability, developer experience, data quality, operational excellence, and cost efficiency through architectural guardrails, platform metrics, observability, operational playbooks, and continuous user feedback;
• Leverage modern Generative AI technologies and emerging AI engineering capabilities to accelerate software delivery, automate engineering workflows, and enable intelligent enterprise solutions that create lasting business value;
• Bring demonstrated experience delivering scalable AI, machine learning, Generative AI, APIs, and enterprise data applications into production environments with a strong focus on engineering excellence and operational reliability.

The key Moderna Mindsets you'll need to succeed in the role

• We obsess over learning. We don’t have to be the smartest we have to learn the fastest;
• We digitize everywhere possible using the power of code to maximize our impact on patients.

Here’s What You’ll Need (Basic Qualifications)

• Bachelor's or Master's degree in Computer Science, AI, Data Science, Engineering, Applied Mathematics, Physics, or a related technical field; equivalent deep technical experience will be considered;
• 7+ years of software, data, or AI engineering experience, including 4+ years delivering production ML, GenAI, analytics, automation, or data-intensive systems;
• Expert proficiency with Python and SQL, strong software architecture fundamentals, and experience with at least one additional language such as Java, Scala, or TypeScript;
• Hands-on frontend engineering experience with TypeScript/JavaScript and a modern framework such as React, Angular, Vue, or Svelte, including reusable components, state management, forms, charts, browser fundamentals, accessibility, and responsive design;
• Hands-on backend engineering experience designing production APIs and services using Python, Node.js, Java, Scala, or comparable technologies, including REST/GraphQL patterns, service boundaries, authentication, authorization, testing, observability, and production debugging;
• Demonstrated experience architecting and operating LLM/GenAI systems, including RAG, agents/tool use, embeddings/vector search, model and prompt evaluation, guardrails, monitoring, and human-in-the-loop controls;
• Strong background in cloud architecture on AWS, Azure, or GCP, including IAM, networking, secrets management, CI/CD, infrastructure-as-code, and production support models;
• Experience designing secure, compliant, auditable systems in regulated environments, with practical understanding of GxP, data integrity, validation documentation, and change control.

Here’s What You’ll Bring to the Table (Preferred Qualifications)

• Experience in life sciences, pharmaceutical manufacturing, supply chain, quality, or another regulated operational domain;
• Experience with SAP-centric supply chain data and integration patterns, including SAP ECC/S/4HANA, IBP, MM, PP, QM, SD, EWM/WM, MDG, BTP, or related analytics enablement;
• Advanced experience with Databricks Asset Bundles, Terraform or infrastructure-as-code, Databricks SQL, Feature Engineering/Feature Store, Vector Search, model monitoring, and lakehouse governance patterns;
• Applied expertise in forecasting, optimization, simulation, anomaly detection, scheduling, inventory, logistics, or decision intelligence methods;
• Track record of mentoring senior engineers, shaping engineering communities of practice, and influencing technical direction across global teams.