Machine Learning Engineer

Machine Learning Engineer | Hybrid (2 days/week from office)You will:Design, develop and maintain high-quality, production-grade Python code for AI/ML applicationsOptimize AI/ML workloads across CPU and GPU infrastructure, balancing performance, scalability and costDesign, automate and operate scalable CI/CD and MLOps pipelines on cloud platforms (GCP and Azure)Build reusable frameworks and tooling for model deployment, monitoring and lifecycle managementDesign and implement scalable data ingestion, transformation and feature pipelinesDesign and operate scalable model-serving and inference systems, including real-time and batch inferenceSupport the productionization of Generative AI and LLM-based applications, including deployment, observability and cost optimizationCollaborate with Data Scientists, Domain Experts and Product Owners on end-to-end AI/ML productsYou bring:3+ years of relevant industry experience in data science, ML engineering, software engineering or MLOpsStrong software engineering skills with proficiency in PythonExperience with containerization (Docker) and KubernetesExperience developing and deploying cloud-native applications on GCP and/or AzureExperience with orchestration platforms such as Apache Airflow, Kubeflow or equivalentProficiency with DevOps practices, including CI/CD and Git-based workflowsHands-on experience designing and supporting RESTful APIs and model-serving servicesSolid understanding of machine learning and deep learning concepts, with production deployment experienceNice to have:Experience with PyTorch and/or TensorFlowFamiliarity with distributed data processing (Spark, Kafka, Flink)Experience with distributed data platforms and large-scale data processing