Overview

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., 9 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, hands-on Full-Stack AI Engineer / Data Scientist with a deep sense of ownership. Rather than being a

Responsibilities

• Design and build advanced AI agentic systems, state machines, search-based 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 end-user applications focusing on relevance and real-time performance.
• Leverage AI coding agents (e.g., Claude Code) daily to accelerate full-stack development cycles, maintaining high productivity across frontend, backend, and infrastructure tasks.
• Take end-to-end accountability for features: write high-quality, production-ready 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 high-throughput, reliable cloud-native solutions on AWS/multicloud.
• Design robust data pipelines to curate, preprocess, and structure diverse datasets that maximize LLM effectiveness and reduce bias.
• Design, customize, optimize, and fine-tune LLM-based and traditional AI algorithms for specific use cases (e.g., text generation, summarization, AI agents, sequence modeling).
• Lead advanced prompt engineering strategies, utilizing zero-shot, few-shot and other paradigms to optimize model outputs without extensive fine-tuning.
• Implement pre-generative AI models (e.g., classification, clustering, regression) when they provide a more efficient, interpretable, or cost-efficient solution compared to LLMs.
• Optimize model inference speed, reduce latency (cold start reduction, caching strategies), and manage resource usage across cloud architectures.
• 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 real-time algorithms monitoring and observability practices, ensuring visibility into pipelines behavior, drift detection, and anomaly identification using telemetry tools.

Qualifications

• 7+ years of experience in AI/ML engineering and Data Science, with exposure to generative AI, agents and classical ML.
• 3+ years of hands-on experience with generative large language models and agents.
• Proven experience managing the end-to-end MLOps lifecycle and deploying models at scale.
• Proven experience leveraging AI coding agents within an agentic SDLC to accelerate feature delivery.
• Expert Python: Advanced production-grade proficiency in Python.
• Cloud Platforms: Practical experience designing cloud solutions on AWS (familiarity with multicloud is a plus), utilizing GenAI-specific services (e.g., Amazon Bedrock, SageMaker).
• AI/GenAI Frameworks: Deep expertise with LangChain, LangGraph or similar LLM orchestration frameworks, agentic system design, and state machine logic.
• Building production-level AI systems including RAGs, Vector DBs, MCP, end-users, integrations, etc., but also observability, DevOps, durable and available system designs.
• Software Engineering Best Practices: Expertise in API design, CI/CD, Docker, Infrastructure as Code, and rigorous automated testing.
• aSDLC expertise: Proven experience in leveraging agents for SDLC (software development lifecycle).
• Databases: Snowflake, SQL, NoSQL, and data lakes.
• Data Skills: Strong background in statistics, data manipulation, synthetic data generation, and feature engineering.
• Could-have:
• Familiarity with TypeScript is considered an advantage - to have a common language with the full-stack Software Engineers.
• Regulatory Compliance: Proven experience in working within highly regulated industries.
• 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: Abilities 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 mitigation, and responsible AI deployment.
• Be deeply passionate about AI, staying continuously up-to-date with the latest developments in foundational models, agentic approaches, and classical ML.
• Be team-oriented, proactive, and collaborative, thriving in a fast-paced environment where roles are fluid and multidisciplinary.
• Be a great communicator, able to present complex findings clearly to both technical and non-technical audiences.
• Be able to communicate in English at the level of: C1+.