2 Kubeflow Pipelines Jobs

About Chargebee: Chargebee is a subscription billing and revenue management platform powering some of the fastest-growing brands around the world today, including Calendly, Hopin, Pret-a-Manger, Freshworks, Okta, Study.com and others. Thousands of SaaS and subscription-first businesses process over billions of dollars in revenue every year through the Chargebee platform. Headquartered in San Francisco, USA, our 500+ team members work remotely throughout the world, including India, the Netherlands, Paris, Spain, Australia, and the USA. Chargebee has raised over $480 million in capital and is funded by Accel, Tiger Global, Insight Partners, Steadview Capital, and Sapphire Ventures. And were on a mission to push the boundaries of subscription revenue operations. Not just ours, but every customer and prospective business on a recurring revenue model. Our team builds high-quality and innovative software to enable our customers to grow their revenues powered by the state-of-the-art subscription management platform. Key Roles & Responsibilities Productionise ML workflows : build and maintain data pipelines for feature generation, ML model training, batch scoring, and real?time inference using modern orchestration and container frameworks. Own model serving infrastructure : implement fast, reliable APIs / batch jobs; manage autoscaling, versioning, and rollback strategies Feature?store development : design and operate feature stores and corresponding data pipelines to guarantee trainingserving consistency. CI/CD & DevEx : automate testing, deployment, and monitoring of data and model artefacts; provide templated repos and documentation that let data scientists move from notebook to prod quickly. Observability & quality : instrument data?drift, concept?drift, and performance metrics; set up alerting dashboards to ensure model health. Collaboration & review : work closely with data scientists on model experimentation, production?harden their code, review PRs, and evangelise MLOps best practices across the organisation. Required Skills & Experience 3+ years as a ML / Data Engineer working on large-scale, data-intensive systems in cloud environments (AWS, GCP, or Azure), with proven experience partnering closely with ML teams to deploy models at scale. Proficient in Python plus one of Go / Java / Scala; strong software?engineering fundamentals (testing, design patterns, code review). Hands on experience in Spark and familiarity with streaming frameworks (Kafka, Flink, Spark Structured Streaming) Hands-on experience with workflow orchestrators (Airflow, Dagster, Kubeflow Pipelines, etc.) and container platforms (Docker + Kubernetes/EKS/ECS). Practical knowledge of ML algorithms like XGBoost, LightGBM, transformers and deep learning frameworks like pytorch is preferred Experience with experiment?tracking / ML model?management tools (MLflow, SageMaker, Vertex AI, Weights & Biases) is a plus Benefits: Want to know what it means to work for a company that genuinely cares about you Check out just a few of the benefits we give our employees: We are Globally Local With a diverse team across four continents, and customers in over 60 countries, you get to work closely with a global perspective right from your own neighborhood. We value Curiosity We believe the next great idea might just be around the corner. Perhaps its that random thought you had ten minutes ago. We believe in creating an ecosystem that fosters a desire to seek out hard questions, and then figure out answers to them. Customer! Customer! Customer! Everything we do is driven towards enabling our customers growth. This means no matter what you do, you will always be adding real value to a real business problem. Its a lot of responsibility, but also a lot of fun. If you resonate with Chargebee, have a monstrous appetite for curiosity, and an insatiable urge to learn and build new things, were waiting for you! We value people from all backgrounds and are dedicated to hiring and employing a diverse and inclusive workplace. Come be a part of the Chargebee tribe! Show more Show less

You will play a vital role as an AI/ML Engineer in a pioneering PropTech startup based in Dubai. The project involves developing a cutting-edge digital real estate platform that integrates AI/ML, data analytics, Web3/blockchain, and conversational AI for long-term sales and short-term stays. Your primary responsibility will be to operationalize machine learning models ensuring they are scalable and reliable for our innovative features. Your tasks will include designing and maintaining scalable infrastructure for training and deploying ML models, creating low-latency APIs for production use, managing data pipelines, and overseeing the MLOps lifecycle. Collaboration with data scientists, backend developers, and product managers will be essential to ensure efficient delivery of AI-driven features. Key Responsibilities: - Design, build, and maintain scalable infrastructure for training and deploying machine learning models. - Operationalize ML models such as the "TruValue UAE" AVM and property recommendation engine by creating robust APIs. - Develop and manage data pipelines to provide clean and reliable data for training and real-time inference. - Implement and manage the MLOps lifecycle including CI/CD for models, monitoring for model drift, and automated retraining. - Optimize the performance of ML models for speed and cost-efficiency in a cloud environment. - Collaborate with backend engineers to integrate ML services with the core platform architecture. - Work with data scientists to enhance model efficacy and feasibility. - Build the technical backend for the AI-powered chatbot, integrating it with NLP services and platform data. Required Skills and Experience: - 3-5+ years of experience in Software Engineering, Machine Learning Engineering, or related roles. - Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. - Strong proficiency in Python and software engineering fundamentals. - Experience deploying ML models in a production environment on major cloud platforms. - Hands-on experience with ML frameworks like TensorFlow, PyTorch, and Scikit-learn. - Experience with data pipelines using tools such as Apache Airflow, Kubeflow Pipelines, or cloud-native solutions. - Collaboration with cross-functional teams to integrate AI solutions into products. - Experience with cloud platforms and containerization (AWS, Azure, GCP, Docker, Kubernetes). Preferred Qualifications: - Experience in PropTech or FinTech sectors. - Direct experience with MLOps tools and platforms (MLflow, Kubeflow, AWS SageMaker, Google AI Platform). - Familiarity with big data technologies (Spark, BigQuery, Redshift). - Experience in building real-time machine learning inference systems. - Strong understanding of microservices architecture. - Experience working collaboratively with data scientists.,