Experience with productionizing ML models and integrating them into real-time and batch applications. Work closely with data science partners and domain engineering teams to understand AI/ML use cases and build corresponding foundations for personalization, NLP, forecasting, recommendations, and generative AI.
Experience using a popular programming language such as: Python (preferred), Java, C#, Scala, etc.
Solid understanding of modern software development practices including object-oriented programming, concurrency, design patterns, microservices architecture, RESTful API design, and test-driven development.
Experience with software testing concepts and tools: white/black box testing, coverage analysis, mocking, PyTest/xUnit, Selenium, etc.
Hands-on experience with GCP technologies such as BigQuery, GKE, GCS, DataFlow, Kubeflow, and Vertex AI — or equivalent services in AWS and/or Azure.
Familiarity with ML frameworks and tools such as TensorFlow, PyTorch, MLflow, scikit-learn, HuggingFace Transformers, etc.
Strong experience with Infrastructure-as-Code and CI/CD tools used to automate deployment to cloud platforms: Terraform, GitHub Actions, Concourse, Ansible, etc.
Excellent leadership, decision-making, and communication skills, with the ability to translate business needs into engineering outcomes.
Experience with AI product lifecycle management, including experimentation tracking, model governance, and release management.