Data Engineer - ML & GenAI Platform
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The role
Line 1: Build, operate, and scale data and ML platforms powering model training, LLM fine-tuning, inference, and evaluation.
Line 2: Design scalable, reliable data architectures and pipelines for training, inference, and evaluation, including batch processing and knowledge-base workflows.
Line 3: Develop and maintain Airflow DAGs for metrics computation, batch inference, KB workflows, and data science projects.
Line 4: Collaborate with data scientists and engineers to productionize models and GenAI features across services.
Line 5: Ensure high-quality code through testing, reviews, documentation, and CI/CD for data and ML workloads.
Line 6: Leverage tools such as Airflow, Spark, Docker, Python Serverless, Kafka, and cloud environments (AWS/GCP) to deliver scalable GenAI solutions.
Line 2: Design scalable, reliable data architectures and pipelines for training, inference, and evaluation, including batch processing and knowledge-base workflows.
Line 3: Develop and maintain Airflow DAGs for metrics computation, batch inference, KB workflows, and data science projects.
Line 4: Collaborate with data scientists and engineers to productionize models and GenAI features across services.
Line 5: Ensure high-quality code through testing, reviews, documentation, and CI/CD for data and ML workloads.
Line 6: Leverage tools such as Airflow, Spark, Docker, Python Serverless, Kafka, and cloud environments (AWS/GCP) to deliver scalable GenAI solutions.
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