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:

See also