5 of 5 Remote/Hybrid Kubeflow Jobs in the UK

. Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure ...

generative AI tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows) MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent) Cloud experience (AWS, GCP, Azure), containerisation and basic data engineering/SQL Familiarity with experimentation, A/B testing and online ...

Azure ML), including sovereign, on-premise and hybrid deployment models. Proficiency in MLOps tools and practices: CI/CD, monitoring and orchestration frameworks (e.g. Kubeflow, Flyte, MLflow). Good knowledge of Docker and Kubernetes for containerizing AI workloads. Understanding of LLM inference stacks (vLLM, llama.cpp, OpenVINO) and model deployment formats ...

Studio, OpenAI, AKS), GCP (Vertex AI, Cloud Run), AWS (Bedrock, SageMaker) Experience building and automating AI/ML pipelines using tools such as MLflow, Kubeflow, Azure ML, Vertex Pipelines, Airflow or Google ADK Hands-on experience with Generative and Agentic AI frameworks such as LangChain, LlamaIndex, CrewAI, Autogen, Google ...

Desirable (AI & Data) Vector DBs: Milvus, Weaviate, Pinecone Data orchestration: Airflow, Prefect, Dagster Search: ElasticSearch/OpenSearch AI frameworks: LangChain, Hugging Face MLOps: MLflow, Kubeflow If you are looking for an opportunity to work on mission-critical systems at the forefront of UK National Security? Apply ...