6 of 6 Remote Apache Spark Jobs in Glasgow

efficient, production-grade AI/ML code. LLMOps & AI Model Management: Experience with tools like MLFlow, LangChain, Hugging Face, Kubeflow, or similar platforms. Data Processing: Proficient with Databricks/Spark for large-scale AI data processing. SQL: Strong capabilities in data querying and preparation. Data Architectures: Understanding of modern data infrastructure (lakehouses, data lakes, vector databases). Additional (Nice More ❯

learn, Hugging Face Transformers, Keras, etc.) Familiarity with Python for AI/ML development is a significant advantage. Experience with data engineering pipelines or big data technologies (e.g., Kafka, Spark) is a plus. The recruitment process you can expect for this role is an initial call with your dedicated Talent Acquisition Partner who will chat with you about Waracle More ❯

Hugging Face Transformers, Keras, etc.). Familiarity with Python for AI/ML development is a significant advantage. Experience with data engineering pipelines or big data technologies (e.g., Kafka, Spark) is a plus. The recruitment process you can expect for this role is an initial call with your dedicated Talent Acquisition Partner who will chat with you about Waracle More ❯

functional teams. Knowledge of innovative software practices (SCRUM/Agile, microservices, containerization like Docker/Kubernetes). we'd also encourage you to apply if you possess: Experience with Spark/Databricks. Experience deploying ML models via APIs (e.g., Flask, Keras). Startup experience or familiarity with geospatial and financial data. The Interview Process (subject to change): You’ll More ❯

Job Description Compensation £60,000 – £80,000 base salary + performance-related bonus + benefits Performance-Related Bonus Benefits (listed below) TL;DR Role: Senior AI Engineer (Agentic AI & LLM Focus) Location: UK-based, fast-growing technology consultancy specialising in More ❯

products includes on-premise cloud solutions such as Openstack, MicroCloud and Ceph, and solutions that could be deployed either on-premises or in public clouds such as Kubernetes, Kubeflow, Spark, PostgreSQL, etc. The team works hands-on with the technologies by deploying, testing and handing over the solution to our support or managed services team at the end of More ❯