Machine Learning Engineer

Hybrid work arrangement: 2 days per week from the office.

Description

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.

Requirements

You bring:

• 3+ 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.