8 of 8 Apache Airflow Jobs in Scotland

experience in data engineering or a related field, with a focus on building scalable data systems and platforms. Expertise in modern data tools and frameworks such as Spark, dbt, Airflow, Kafka, Databricks, and cloud-native services (AWS, GCP, or Azure) Understanding of data modeling, distributed systems, ETL/ELT pipelines, and streaming architectures Proficiency in SQL and at least More ❯

Meta, Amazon , OpenAI) Proficiency with essential data science libraries including Pandas, NumPy, scikit-learn, Plotly/Matplotlib, and Jupyter Notebooks Knowledge of ML-adjacent technologies, including AWS SageMaker and Apache Airflow. Strong skills in data preprocessing, wrangling, and augmentation techniques Experience deploying scalable AI solutions on cloud platforms (AWS, Google Cloud, or Azure) with enthusiasm for MLOps tools and More ❯

fine-tuning techniques including LoRA, QLoRA, and parameter efficient methods - Multi-modal AI systems combining text, image, and structured data - Reinforcement Learning from Human Feedback (RLHF) for model alignment - Apache Airflow/Dagster for ML workflow orchestration and ETL pipeline management - Model versioning and experiment tracking (MLflow, Weights & Biases) - Real-time model serving and edge deployment strategies - A More ❯

fine-tuning techniques including LoRA, QLoRA, and parameter efficient methods - Multi-modal AI systems combining text, image, and structured data - Reinforcement Learning from Human Feedback (RLHF) for model alignment - Apache Airflow/Dagster for ML workflow orchestration and ETL pipeline management - Model versioning and experiment tracking (MLflow, Weights & Biases) - Real-time model serving and edge deployment strategies - A More ❯

fine-tuning techniques including LoRA, QLoRA, and parameter efficient methods - Multi-modal AI systems combining text, image, and structured data - Reinforcement Learning from Human Feedback (RLHF) for model alignment - Apache Airflow/Dagster for ML workflow orchestration and ETL pipeline management - Model versioning and experiment tracking (MLflow, Weights & Biases) - Real-time model serving and edge deployment strategies - A More ❯

Transformers , PyTorch , YOLO , OpenCV , and Pillow Designing and deploying AI services via FastAPI , AWS Lambda , EKS/ECS , and S3 Creating scalable data pipelines with Pandas , NumPy , SQLAlchemy , and Airflow Supporting real-time model inference, edge deployments, and API integrations Working closely with a technical lead in a service-based and serverless architecture Essential Python Engineer skills and experience More ❯

systems. Deploy scalable AI systems using AWS (Lambda, S3, SQS, EKS/ECS), CDK , and modern DevOps practices. Collaborate on infrastructure and data pipelines using SQLAlchemy, Boto3, Pandas, and Airflow . Contribute to real-time AI services , model versioning, and advanced fine-tuning (LoRA, QLoRA, etc.). Qualifications Solid Python skills (3.9+) with deep knowledge of async, OOP, and More ❯

systems. Deploy scalable AI systems using AWS (Lambda, S3, SQS, EKS/ECS), CDK , and modern DevOps practices. Collaborate on infrastructure and data pipelines using SQLAlchemy, Boto3, Pandas, and Airflow . Contribute to real-time AI services , model versioning, and advanced fine-tuning (LoRA, QLoRA, etc.). AI Engineer requirements: Solid Python skills (3.9+) with deep knowledge of async More ❯