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Builds open-source data platforms using Python and Kubernetes, integrating ML tools like Kubeflow and MLFlow for cloud and private infrastructure.
Build and deploy AI models for human-like conversations, spanning speech, language, and real-time inference systems. Own end-to-end ML pipelines and MLOps tooling in a fast-moving startup.
Designs and operates scalable AI infrastructure, automates ML deployment pipelines, and implements DevSecOps for secure AI-enabled software in homeland security.
Build and scale enterprise-grade ML and GenAI platforms, hardening research code into production services, and enabling hundreds of practitioners to deploy and monitor models securely and cost-effectively.
Senior Data Engineer builds secure, scalable pipelines and cloud data infrastructure to power real-time analytics and ML models for a cleantech firm optimizing energy storage and costs.
Principal ML Engineer at Grab’s AI Platform team, building and scaling ML infrastructure for Southeast Asia’s superapp, including LLM training/serving, fraud detection, and search ranking.
Build and maintain an internal AIOps/ML/LLM platform, including Kubernetes infrastructure, ML workflows, and production deployment for cybersecurity use cases.
Build and productionize generative AI models and LLM-driven applications using PyTorch, RAG pipelines, and vector databases, while engineering robust MLOps and data pipelines in Python.
Build and maintain cloud-native ML pipelines and automation for AI model deployment using tools like MLflow, Kubeflow, and AWS SageMaker.
Build and maintain AI/ML pipelines, deploy models, and manage lifecycle with tools like MLflow, Kubeflow, or cloud platforms (AWS SageMaker/Azure ML).
Builds and maintains data pipelines and ML workflows in Python, using Airflow/Kubeflow and Docker/Kubernetes on GCP to process large datasets.
Builds and maintains data pipelines and ETL workflows in Python, running on Google Cloud, to process large datasets efficiently.
Build and operate a sovereign European cloud platform using Kubernetes, GitOps, and CNCF tools; design lifecycle, CI/CD, and observability for a PaaS stack powering science and Earth Observation.
Lead the design and implementation of scalable GenAI, LLM, and AI-agent architectures, including RAG and MLOps/LLMOps pipelines, to deliver enterprise solutions and accelerate client adoption.
Lead the design and delivery of end-to-end scalable machine learning systems using Python, TensorFlow, and cloud platforms, while mentoring teams and shaping ML strategy for high-stakes client projects.
Design and build production-grade AI/ML platforms and MLOps pipelines for clients, using Python, cloud (GCP/AWS/Azure), Terraform, and tools like Vertex AI and Kubernetes.
Build and deploy ML models and LLM-based apps for beauty ecommerce, including recommendation engines and NLP, using PyTorch, FastAPI, and GCP VertexAI.
Build and deploy AI models (computer vision, ML) and data pipelines to automate avionics workflows and deliver data-driven insights for Thales’ flight systems.
Builds and maintains scalable backend services in Go/Python and integrates ML models into production for AI/LLM-powered products, while also developing React-based UIs.
Build and maintain cloud-native infrastructure for AI/ML platforms using Kubernetes, Terraform, and CI/CD pipelines, collaborating with engineering and data teams to optimize deployments.
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