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 applications
• Optimize AI/ML workloads across CPU and GPU infrastructure, balancing performance, scalability and cost
• Design, 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 management
• Design and implement scalable data ingestion, transformation and feature pipelines
• Design and operate scalable model-serving and inference systems, including real-time and batch inference
• Support the productionization of Generative AI and LLM-based applications, including deployment, observability and cost optimization
• Collaborate with Data Scientists, Domain Experts and Product Owners on end-to-end AI/ML products

You bring:

• 4+ years of relevant industry experience in data science, ML engineering, software engineering or MLOps
• Strong software engineering skills with proficiency in Python
• Experience with containerization (Docker) and Kubernetes
• Experience developing and deploying cloud-native applications on GCP and/or Azure
• Experience with orchestration platforms such as Apache Airflow, Kubeflow or equivalent
• Proficiency with DevOps practices, including CI/CD and Git-based workflows
• Hands-on experience designing and supporting RESTful APIs and model-serving services
• Solid understanding of machine learning and deep learning concepts, with production deployment experience

Nice to have:

• Experience with PyTorch and/or TensorFlow
• Familiarity with distributed data processing (Spark, Kafka, Flink)
• Experience with distributed data platforms and large-scale data processing