FullStack AI Engineer Data Scientist Agentic 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 About the Role At Roche Digital Technology we are advancing the boundaries of Applied AI The Applied AI Use Case Engineering Operations Team is tasked with building innovative AI applications GenAI agents and agentic foundations In the 2026 tech landscape the lines between traditional disciplines have blurred We operate in small agile teams e g 6 members powered by advanced coding agents like Claude Code to develop and ship solutions faster than ever before We are looking for a highly skilled handson FullStack AI Engineer Data Scientist with a deep sense of ownership Rather than being a narrowly specialized Data Scientist ML Engineer or MLOps Engineer you will combine these skill sets You will build agentic generative AI systems and classical ML systems endtoend taking accountability from concept and exploratory data analysis all the way to production releases and monitoring Core Tech Stack Scope This role involves working deeply with multimodal foundation models advanced agentic workflows including AgenttoAgent A2A communication Model Context Protocol MCP RetrievalAugmented Generation RAG pipelines other emerging AI technologies and MLOps subsystems You will utilize cloud services AWS and multicloud alongside vector graph and traditional databases to develop scalable and robust AI solutions Key Responsibilities Agentic GenAI Application Development Design and build advanced AI agentic systems state machines searchbased conversational systems that solve complex business problems Develop workflows leveraging Large Foundational Multimodal Models to process and reason across text audio and video modalities Implement Model Context Protocol MCP servers clients to standardize context exchange between agents data sources and external tools Collaborate with AI Architects Product Owners and fellow developers to integrate AI capabilities into scalable fair and ethical enduser applications focusing on relevance and realtime performance FullStack Engineering Agentic SDLC Leverage AI coding agents e g Claude Code daily to accelerate fullstack development cycles maintaining high productivity across frontend backend and infrastructure tasks Take endtoend accountability for features write highquality productionready Python and occasionally TypeScript code with comprehensive testing and documentation Manage the DevOps MLOps lifecycle containerize applications using Docker configure CI CD pipelines and architect highthroughput reliable cloudnative solutions on AWS multicloud Data Science EDA Strategy Perform thorough Exploratory Data Analysis EDA to understand dataset characteristics uncover patterns detect biases and identify data quality issues Use statistical and visualization techniques to inform feature engineering model selection and optimization of foundation modelbased applications Design robust data pipelines to curate preprocess and structure diverse datasets that maximize LLM effectiveness and reduce bias Algorithm Development Optimization Design customize optimize and finetune LLMbased and traditional AI algorithms for specific use cases e g text generation summarization AI agents sequence modeling Lead advanced prompt engineering strategies utilizing zeroshot fewshot and other paradigms to optimize model outputs without extensive finetuning Implement pregenerative AI models e g classification clustering regression when they provide a more efficient interpretable or costeffective solution compared to LLMs Optimize model inference speed reduce latency cold start reduction caching strategies and manage resource usage across cloud architectures Evaluation Observability Continuous Improvement Conduct rigorous experimentation A B testing and implement automatic metric pipelines e g BLEU ROUGE RAG retrieval accuracy human rating frameworks etc to evaluate generative and multimodal systems Implement realtime algorithms monitoring and observability practices ensuring visibility into pipelines behavior drift detection and anomaly identification using telemetry tools Translate complex technical results into clear actionable insights for stakeholders driving datadriven decisionmaking Practical Skills Required Experience 7 years of experience in AI ML engineering and Data Science with exposure to generative AI agents and classical ML 3 years of handson experience