1 to 25 of 32 Permanent Amazon SageMaker Jobs in the UK

strong expertise in AWS-native services, infrastructure automation, and end to end ML workflow operations. The ideal candidate will have deep experience with CloudFormation, SageMaker, Glue, Lambda, Spark, and Python, along with solid understanding of machine learning concepts, data pipelines, model monitoring, and production deployment. Key Responsibilities • Design, automate … maintain scalable ML infrastructure using AWS CloudFormation and AWS-native services. • Build, deploy, and manage ML models on Amazon SageMaker (training, tuning, hosting, endpoints, pipelines). • Develop and optimize distributed data processing workflows using Apache Spark, AWS Glue, and related ETL frameworks. • Build serverless automation and integration logic ...

large language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data ...

engineers What We're Looking For: Proficiency in Python and ML frameworks (scikit-learn, PyTorch, TensorFlow) Hands-on experience with AWS ML services (SageMaker, Lambda) Familiarity with containerisation (Docker) and orchestration (Kubernetes/ECS) Knowledge of LLMOps and GenAI tools (LangChain, LangSmith) Understanding of feature engineering and vector databases ...

inference Advanced Python skills (NumPy, pandas, scikit-learn, PyTorch or TensorFlow) Experience building, deploying and optimising ML models in production Strong AWS experience (e.g. SageMaker, Lambda or similar services) Expert SQL skills and experience working with large, complex datasets Solid data engineering fundamentals, including pipelines and APIs Comfortable with ...

orchestration tools like Kubeflow, Airflow, or Metaflow. Cloud Architecture: Expertise in deploying ML workloads on AWS, GCP, or Azure, utilizing managed services like SageMaker or Vertex AI. Data Foundations: Strong understanding of data modeling, feature stores, and distributed computing (e.g., Spark, Dask). Qualifications & Experience Experience: 5+ years ...

related technical discipline Bonus Skills/Experience Understanding of MLOps, model serving, scaling and monitoring workflows (e.g., BentoML, MLflow, Vertex AI, AWS Sagemaker) Experience building streaming + batch data ingestion and transformation pipelines (Spark/Airflow/Beam) Mobile and front-end web application development experience What We Offer ...

Computer Science, Machine Learning, Engineering , or a closely related discipline. Bonus Points Experience with MLOps and model serving platforms such as BentoML, MLflow, or SageMaker . Hands-on work with streaming and batch data pipelines using tools like Spark, Airflow, or Apache Beam . Background in front ...

marketplace or matching algorithms. Exposure to flexibility markets, virtual power plants, or reconciliation/settlement processes. Experience with ML deployment frameworks (MLflow, Vertex AI, SageMaker). Knowledge of optimisation libraries such as Gurobi, OR‐Tools, or Pyomo. ...

Generative AI models, LLMs, and agentic frameworks. •Collaborate with stakeholders to define problem statements and solution approaches. •Deploy models using MLOps tools such as SageMaker, Snowflake, and CI/CD pipelines. •Integrate vector databases and Retrieval-Augmented Generation (RAG) pipelines. •Ensure high-quality delivery and adherence to best practices ...

expert should have: A strong academic background in data science, computer science, mathematics, or a related field. Hands-on experience with AWS ML stack (SageMaker, Lambda, Redshift). Proven ability to design and implement machine learning algorithms and models. Proficiency in Python, SQL, and ML libraries (e.g., scikit-learn ...

Azure Familiarity with Spark, Kafka, Airflow, Docker, and Kubernetes Nice to Have Experience with vector databases (Pinecone, FAISS, Weaviate) ML platforms like MLflow, Kubeflow, SageMaker RAG, embeddings, or knowledge graphs ...

have a background in C#) Knowledge of AI principles and AI ethics Knowledge of data safety in LLM usage Experience with: AWS: boto3, Bedrock, SageMaker, Lambda, S3, EC2 Azure: Azure OpenAI Service, Cosmos DB Retrieval-Augmented Generation (RAG), Graph RAG Embedding models and LLM training fundamental ...

technical stakeholders Mentor junior team members Skillset: Strong Python and ML libraries (scikit-learn, PyTorch, pandas, NumPy) Experience with AWS cloud services (SageMaker, Lambda, S3) Familiarity with MLOps tools (MLflow, Weights & Biases) Knowledge of NLP, computer vision, or time-series analysis Ability to design experiments and apply statistical validation ...

learning concepts (classification, regression, clustering). Knowledge of big data platforms and cloud-based AI analytics environments (e.g., Databricks, Snowflake, Azure ML, or AWS SageMaker). Ability to present complex issues as simple, compelling, and data-driven narratives, enhanced by AI-supported visualization and summarization tools. Demonstrated curiosity ...

Experience working in an agile delivery environment Strong problem‐solving skills and attention to detail Desired Skills: Experience with cloud‐based ML services (AWS SageMaker, Azure ML, Databricks) Familiarity with MLOps practices and model lifecycle management Experience with NLP, deep learning, or computer vision Knowledge of data visualisation tools ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

e.g. seaborn) Proficient in working with cloud platforms (AWS preferred), code versioning (Git), experiment tracking (e.g. MLflow) Experience with cloud-based ML tools (e.g. SageMaker), data and model versioning (e.g. DVC), CI/CD (e.g. GitHub Actions), workflow orchestration (e.g. Airflow/Dagster) and containerised solutions (e.g. Docker ...

e.g. seaborn) Proficient in working with cloud platforms (AWS preferred), code versioning (Git), experiment tracking (e.g. MLflow) Experience with cloud-based ML tools (e.g. SageMaker), data and model versioning (e.g. DVC), CI/CD (e.g. GitHub Actions), workflow orchestration (e.g. Airflow/Dagster) and containerised solutions (e.g. Docker ...

e.g. seaborn) Proficient in working with cloud platforms (AWS preferred), code versioning (Git), experiment tracking (e.g. MLflow) Experience with cloud-based ML tools (e.g. SageMaker), data and model versioning (e.g. DVC), CI/CD (e.g. GitHub Actions), workflow orchestration (e.g. Airflow/Dagster) and containerised solutions (e.g. Docker ...

LLMs (Large Language Models), generative models, and related frameworks (e.g., LangChain, TensorFlow, etc.) Possess knowledge of cloud-based AI/ML services like AWS SageMaker, Azure Machine Learning, Google AI Platform, Databricks GenAI/BI, Snowflake GenAI, and their relevant services for building and deploying GenAI workloads. Knowledge ...

similar large-scale ranking models. Generative AI & Technical Proficiency: Hands-on experience with LLM fine-tuning, Python, modern NLP frameworks, and familiarity with AWS SageMaker, Docker, and Airflow. Strong mathematical and statistical foundation. Ethical Drive: Commitment to ethical and fair AI practices that ensureequitableoutcomes for all users. Additional Information ...

Experience with Java and automated testing approaches (TDD/BDD) Exposure to Kubernetes, Terraform, and Oracle databases Experience with cloud-based ML platforms (e.g., SageMaker) Desirable: Databricks, Data Lakes, or applied AI use cases Contact Harry – Paritas Recruitment Email: harry@paritasrecruitment.com Mobile: +44 ...

Evidence of defining, building and maintaining infrastructure as code using Terraform. Experience of creating cloud-based solutions - specifically AWS, utilising the likes of Athena, Sagemaker and Glue, as well as associated cloud services. You and your role As our Technical lead, you will lead multi-disciplinary teams ...

Evidence of defining, building and maintaining infrastructure as code using Terraform. Experience of creating cloud-based solutions - specifically AWS, utilising the likes of Athena, Sagemaker and Glue, as well as associated cloud services. You and your role As our Technical lead, you will lead multi-disciplinary teams ...

Evidence of defining, building and maintaining infrastructure as code using Terraform. Experience of creating cloud-based solutions - specifically AWS, utilising the likes of Athena, Sagemaker and Glue, as well as associated cloud services. You and your role As our Technical lead, you will lead multi-disciplinary teams ...