Fullstack Data Engineer - Applied & Agentic AI Systems

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters. The Position Job description Our Applied AI Engineering Team is seeking a fullstack AI Data Engineer to join a newly formed, autonomous development squad dedicated to pioneering agentic, LLM-based solutions. Operating across the entire lifecycle—from initial ideation and rapid prototyping to production-grade deployment and ongoing operations—you will architect the resilient data infrastructure required to power next-generation AI. We are looking for an expert capable of orchestrating both structured and unstructured datasets, implementing high-performance vector databases, and managing real-time streams within cloud-native environments. Description of the area At Roche Digital Technology, we are advancing the boundaries of Applied AI. The Applied AI Engineering Team focuses on architecting, building, and operating high-value AI solutions and services to solve complex business challenges in healthcare. In the 2026 tech landscape, we operate in small, highly autonomous agile teams (e.g., ~9 members) powered by advanced coding agents (like Claude Code) to develop and ship solutions faster than ever before. In this highly regulated environment, quality cannot be an afterthought. You will be the foundational pillar ensuring our rapidly developed agentic workflows and AI, GenAI, and agentic applications are safe, compliant, and robust before they reach the clinical or enterprise user. Job Responsibilities Generative AI Application Co-creation : Collaborate with AI engineers, data scientists, product owners, and other developers in Agile teams to integrate LLMs into scalable, robust, fair, and ethical end-user applications, focusing on user experience, relevance, and real-time performance Data Infrastructure Development and Data Integration : Design and implement scalable, high-performance data pipelines for AI/GenAI applications, ensuring efficient data ingestion, transformation, storage and retrieval; integrate different databases, requiring understanding of data architectures / Domain data ecosystem Vector Databases : work with vector databases (e.g., AWS OpenSearch, Azure AI Search) to facilitate scalable, high-speed similarity search and RAG for generative AI applications with high-dimensional data. Graph Databases : Work with graph databases (e.g., Neo4j, AWS Neptune) to enable GraphRAG, support multi-hop logical reasoning for agentic workflows, and provide auditable explainability for enterprise AI decision-making. Cloud-Based Data Engineering: Build and maintain cloud-based data solutions using AWS (OpenSearch, S3) or Azure (Azure AI Search, Azure Blob Storage) Snowflake Implementation: Design and optimize data storage and processing using Snowflake for scalable, cloud-native analytics solutions Data Processing & Transformation : Develop ETL/ELT pipelines to enable real-time and batch data processing Support AI Model Workflows : Collaborate with AI/ML Engineers and Data Scientists to ensure seamless integration of data pipelines with AI finetuning, inference and training workflows Performance Optimization : Optimize data storage, retrieval, and processing strategies for efficiency, scalability, and cost-effectiveness Software Development Lifecycle : understand and leverage an agentic software development lifecycle (SDLC) in day to day work Security & Compliance : Implement data governance, security best practices, and compliance measures aligned with Roche’s standards Monitoring & Maintenance : Set up monitoring, alerting, and logging for data pipelines, ensuring high availability and reliability Skills Must have: Experience : 7+ years in data engineering, preferably supporting AI/ML applications Advanced Programming & SQL: Writing production-grade code in Python alongside highly optimized, complex SQL queries. Advanced System Architecture & Modeling: Designing scalable, fault-tolerant ETL/ELT data pipelines and Lakehouse architectures (e.g., Snowflake). Orchestration: Hands-on expertise with orchestration tools (like Airflow). Data Engineering in AI: Developing Retrieval-Augmented Generation (RAG), AI systems powered by Vector Databases and/or LLM fine-tuning, and data preparation Document Processing Proficiency : Extracting, transforming, and loading data from diverse file formats (PDF, DOCX, CSV, JSON, etc.), including automated parsing and information retrieval from unstructured and semi-structured documents Version Control & DevOps : Hands-on experience with Git, CI/CD, containerization (Docker, Kubernetes), and Infrastructure as Code (Terraform, CloudFormation) Problem Solving: Excellent analytical skills and the ability to tackle complex challenges with innovative solutions Should have: AWS Cloud Platforms: Hands-on experience with AWS (OpenSearch, S3, Lambda, AWS fundamentals) GraphDB: Experience in building solutions with GraphDB APIs & Microservices : Ability to design and integrate RESTful APIs for data exchange Data Security & Governance : Understanding of encryption and role-based access controls Working in an SDLC environment meeting regulatory requirements Proficiency in best practices of software engineering Agentic SDLC & Engineering Excellence: Leverage AI coding assistants and autonomous agents (e.g., Claude Code, Ona) daily to accelerate full-stack development and testing cycles. Conduct rigorous code reviews for both human-written and AI-generatd code. Could have: Regulatory Compliance: Proven experience in working within highly regulated industries. Data Science & Classical Machine Learning : Practical background in Data Science, encompassing feature engineering, model training, and data preparation leveraging traditional ML techniques. Distributed Data Processing: Hands-on expertise with big data frameworks (like Apache Spark or Flink). Capabilities: Problem-Solving Skills: Excellent analytical skills to tackle complex engineering and statistical challenges. Ownership & Leadership: Deep sense of accountability, eager to define architectural patterns, and able to step into a Tech Lead role when necessary. Consulting: Ability to work closely with stakeholders across the enterprise to consult on the technological approaches to their business problems. Ethics: Strong understanding of biases, fairness, hallucination mitigati