Design, develop and deploy machine learning solutions and services
Implement end-to-end machine learning pipelines from data ingestion to training and model serving
Operationalize LLMs, embeddings, and multi-agent systems in real-world applications
Manage the machine learning and model lifecycle (experimentation, registry, deployment)
Oversee the model promotion lifecycle, coordinating validation gates and approval workflows to safely deploy new model versions from stating to production
Containerize applications using Docker and orchestrate them via Kubernetes
Build and maintain CI/CD pipelines for ML models and LLM applications
Advanced proficiency in Python programming with a focus on writing clean, testable and efficient code
DevOps & Containers: Proficient with Docker for containerization and working knowledge of Kubernetes (k8s) for orchestration
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