<p><strong>MACHINE LEARNING ENGINEER - Switzerland</strong><strong></strong></p><p><strong>Job Title:</strong>Machine Learning Engineer – GenAI, Multi-Agent Systems & MLops Specialist </p><p><strong>Job Description</strong></p><p>We are seeking a skilled Machine Learning Engineer to build and deploy production-grade AI solutions. These solutions may involve traditional ML models, multi-agent AI systems, or a combination of both. The ideal candidate will have hands-on experience developing GenAI solutions with robust AgentsOps—including testing and validation—and a strong background in MLops. You will collaborate with cross-functional teams and clients to translate complex requirements into scalable, high-quality AI systems.</p><p><strong><br></strong></p><p><strong>Responsibilities</strong></p><ul><li><strong>Develop & Deploy:</strong> Build and deploy production-level AI solutions, whether as standalone ML models, multi-agent systems, or integrated approaches.</li><li><strong>GenAI & AgentsOps:</strong> Design and implement GenAI solutions with comprehensive AgentsOps strategies, ensuring rigorous testing and validation.</li><li><strong>MLops Integration:</strong> Develop and maintain MLops pipelines to streamline continuous integration, deployment, and monitoring of AI models.</li><li><strong>Collaboration:</strong> Work closely with clients and internal teams to gather requirements and deliver effective solutions.</li><li><strong>Data Engineering:</strong> Participate in data preprocessing, feature engineering, and the creation of scalable data pipelines.</li></ul><p><strong><br></strong></p><p><strong>Required Qualifications</strong></p><ul><li><strong>Education:</strong> Bachelor’s or Master’s degree in a quantitative field (or equivalent practical experience).</li><li><strong>Production Experience:</strong> Proven experience in building production-grade GenAI solutions, incorporating proper AgentsOps, testing, and validation practices.</li><li><strong>MLops Expertise:</strong> Solid hands-on experience with MLops processes and tools, managing the full lifecycle of AI models in production.</li><li><strong>Technical Skills:</strong><ul><li>Proficiency in Python (or R) and experience with standard ML libraries (e.g., scikit-learn).</li><li>Familiarity with deep learning frameworks such as PyTorch and TensorFlow/Keras.</li><li>Exposure to cloud platforms like GCP, AWS, or Azure and distributed processing frameworks (e.g., Apache Spark).</li><li>Experience with GenAI agents libraries (e.g., <strong>LangChain</strong>, <strong>LangGraph, CrewAI, Autogen</strong>, <strong>LlamaIndex, etc…</strong>) and integrating secure API access to LLM models (e.g., <strong>OpenAI’s</strong>, <strong>Anthropic’s Claude</strong>, <strong>Hugging Face Inference API, Deepseek API, etc…</strong>).</li></ul></li><li><strong>Problem-Solving:</strong> Strong understanding of machine learning algorithms, data pipeline development, and system optimization.</li></ul><p><strong><br></strong></p><p><strong>Preferred Skills</strong></p><ul><li><strong>DevOps:</strong> Experience with CI/CD practices and container orchestration tools (e.g., Kubernetes).</li><li><strong>Advanced AgentsOps:</strong> In-depth knowledge of agent-based system operations.</li><li><strong>Versatility:</strong> Proven track record in working with both traditional ML models and multi-agent AI systems.</li><li><strong>Full Stack Development:</strong> Familiarity with full stack development is a plus, with an emphasis on backend engineering.</li></ul><p><strong><br></strong></p><p><strong>What We Offer</strong></p><p>Join a dynamic, innovative team where you’ll work on cutting-edge AI projects and make a significant impact. We provide an environment that supports continuous learning, collaboration, and professional growth. If you’re passionate about leveraging advanced AI techniques to solve real-world challenges, we’d love to hear from you!</p><p></p> •
Last updated on Dec 16, 2024