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Build and deploy data pipelines, cloud data platforms, and ML systems for clients using Databricks, Spark, AWS, and MLOps tooling.
Design and build scalable data platforms and pipelines to accelerate digital transformation using Python, Databricks, MLflow, and cloud platforms.
Build and deploy ML models end-to-end: design MLOps pipelines, containerize services, and scale AI systems on cloud platforms like AWS/GCP.
Build and maintain data pipelines, deploy AI solutions (LLMs, RAG, assistants), and industrialize ML workflows using Python, Spark, and cloud platforms like Azure.
Senior Data Engineer building scalable data pipelines and AI solutions to accelerate digital transformation at a global industrial group, using Python, Databricks, and cloud platforms.
Build and maintain scalable data pipelines on Databricks, integrating diverse sources and industrializing ML models for enterprise clients.
Senior Data Engineer builds and scales Databricks/AWS pipelines to industrialize analytics and ML prototypes into robust, governed solutions for an energy-sector platform.
Senior Data Engineer builds scalable data pipelines and AI solutions for Nexans, leveraging Python, Databricks, and cloud platforms to drive digital transformation across industrial, financial, and sales domains.
Build and maintain data pipelines, integrate generative AI models, and deploy solutions on cloud platforms using Python, SQL, Databricks, and Azure.
Senior Data Engineer building scalable data pipelines and Lakehouse architectures on Databricks and AWS, using Spark, Python, and ML tools to deliver end-to-end data solutions for enterprise clients.
Build and deploy MLOps pipelines to collect robotics data, orchestrate model training, and automate deployment for AI-driven warehouse automation systems using Python, cloud infra, and Kubernetes.
Designs and deploys scalable data and AI pipelines using Python, Databricks, MLflow, and cloud platforms (Azure/AWS/GCP) to support business use cases.
Senior Data Engineer building scalable data platforms and AI pipelines for industrial use cases using Python, Databricks, MLflow, and cloud infrastructure.
Lead a team to design and build scalable data pipelines and platforms for clients, advising on cloud and big-data tech while enforcing DevOps, FinOps, and governance best practices.
Lead a new DevOps business unit at an engineering agency, building and automating resilient multi-cloud infrastructure (AWS/GCP/Azure) and integrating AI/ML tooling while collaborating with development teams and external partners.
Build and deploy full-stack AI applications using Python backends, React/Angular frontends, and LLM integrations (RAG, agents) with PostgreSQL vector stores and Kubernetes CI/CD pipelines.
Build and deploy robust backend services that expose, orchestrate, and supervise AI solutions like conversational agents, RAG pipelines, and NER extractors in Python.
Lead the design and operation of secure, scalable data and ML pipelines on Domino Data Lab, using Kubernetes, Docker, and CI/CD to deliver reliable, compliant workflows for regulated industries like pharma.
Senior DevOps engineer builds and scales GPU-powered AKS/EKS clusters for LLM inference and fine-tuning, automates CI/CD pipelines, and implements GenAIOps observability and cost controls.
Build and maintain cloud and IoT platforms, CI/CD pipelines, and Azure-based environments; automate device-to-cloud data flows and support MLOps for ML model deployment.
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