19 of 19 Amazon SageMaker Jobs in the UK excluding London

a DataOps or DevOps approach. Demonstrate how to automate and manage data systems so they run smoothly and can grow easily. Experience with tools like AWS (S3, Glue, Redshift, SageMaker) or other cloud platforms. Familiarity with Docker, Terraform, GitHub Actions, and Vault for managing secrets. Experience in coding SQL, Python, Spark, or Scala to work with data. Experience with More ❯

a DataOps or DevOps approach. Demonstrate how to automate and manage data systems so they run smoothly and can grow easily. Experience with tools like AWS (S3, Glue, Redshift, SageMaker) or other cloud platforms. Familiarity with Docker, Terraform, GitHub Actions, and Vault for managing secrets. Experience in coding SQL, Python, Spark, or Scala to work with data. Experience with More ❯

training techniques including fine-tuning, RLHF, parameter-efficient methods (LoRA/QLoRA), or custom post-training workflows MLOps experience : Knowledge and familiarity with MLOps frameworks and tools such as Sagemaker, Kedro, MLflow or Weights and Biases Energy Domain Knowledge: Background in power systems, energy dispatch optimisation, grid modelling, or other energy sector applications where AI/ML drives operational … proficiency with hands-on experience in AI/ML frameworks including RAG, LangChain, TensorFlow, and PyTorch Practical experience with Generative AI and exposure to leading LLM platforms (Anthropic, 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, Kedro and More ❯

on cutting-edge machine learning and data engineering projects, leveraging the latest cloud technologies and MLOps practices. Required/Primary Skills: AWS Data Engineering ML Engineering ML-Ops ECS, Sagemaker Gitlab Jenkins CI/CD AI Lifecycle Experience in front-end development (HTML, Stream-lit, Flask Familiarity with model deployment and monitoring in cloud environments (AWS). Understanding of More ❯

for a Data Engineer for FS domain with Machine learning and MLOps background and proficiency in Python. YOUR PROFILE Primary Skill: AWS Data Engineering, ML Engineering, ML-Ops, ECS, Sagemaker, Gitlab, Jenkins, CI/CD, AI Lifecycle, Experience in front-end development (HTML, Stream-lit, Flask.) Familiarity with model deployment and monitoring in cloud environments (AWS). Understanding of More ❯

for a Data Engineer for FS domain with Machine learning and MLOps background and proficiency in Python. YOUR PROFILE Primary Skill: AWS Data Engineering, ML Engineering, ML-Ops, ECS, Sagemaker, Gitlab, Jenkins, CI/CD, AI Lifecycle, Experience in front-end development (HTML, Stream-lit, Flask.) Familiarity with model deployment and monitoring in cloud environments (AWS). Understanding of More ❯

for a Data Engineer for FS domain with Machine learning and MLOps background and proficiency in Python. YOUR PROFILE Primary Skill: AWS Data Engineering, ML Engineering, ML-Ops, ECS, Sagemaker, Gitlab, Jenkins, CI/CD, AI Lifecycle, Experience in front-end development (HTML, Stream-lit, Flask.) Familiarity with model deployment and monitoring in cloud environments (AWS). Understanding of More ❯

turn insights into product features Ensure compliance, scalability, and reliability of ML systems Skills Required Proven experience with ML in production environments Strong skills in Python, SQL, and AWS (SageMaker, Lambda, etc.) Familiarity with MLOps and real-time data applications A pragmatic mindset and strong problem-solving ability Background in adtech or fraud detection is a bonus Desired Skills More ❯

turn insights into product features Ensure compliance, scalability, and reliability of ML systems Skills Required Proven experience with ML in production environments Strong skills in Python, SQL, and AWS (SageMaker, Lambda, etc.) Familiarity with MLOps and real-time data applications A pragmatic mindset and strong problem-solving ability Background in adtech or fraud detection is a bonus Desired Skills More ❯

turn insights into product features Ensure compliance, scalability, and reliability of ML systems Skills Required Proven experience with ML in production environments Strong skills in Python, SQL, and AWS (SageMaker, Lambda, etc.) Familiarity with MLOps and real-time data applications A pragmatic mindset and strong problem-solving ability Background in adtech or fraud detection is a bonus Desired Skills More ❯

with the organisation's existing team to support the delivery of one of the organisation's key strategic initiatives. Experience required: AWS Data/ML Engineering & ML Ops (ECS, Sagemaker) CI/CD pipelines (GitLab, Jenkins) Python, PySpark & Big Data ecosystems AI/ML lifecycle, deployment & monitoring MLOps tooling (MLflow, Airflow, Docker, Kubernetes) Front-end exposure (HTML, Flask, Streamlit More ❯

integration. Familiarity with CI/CD pipelines and modern DevOps workflows. Understanding of authentication, authorization, and web security best practices. Nice-to-haves: Experience deploying ML models using AWS Sagemaker, Hugging Face, or Replicate . Familiarity with WebGL, THREE.js , or other 3D graphics frameworks. Interest in AI, generative models, and spatial computing . More ❯

and ML engineers to reduce time-to-production for experiments and prototypes. Design and optimize data wrangling and transformation workflows using Python. Leverage AWS cloud services (EC2, S3, Lambda, SageMaker, RDS, DynamoDB, Redshift, etc.) to build robust, scalable, and cost-effective solutions. Apply AIOps practices to enhance monitoring, automation, and resilience of ML systems. Implement best practices in data … software engineering principles. Advanced proficiency in Python for data wrangling, pipeline automation, and ML workflows. Expertise in AWS services, including databases (RDS, DynamoDB, Redshift) and machine learning/AI (SageMaker, AI/ML frameworks). Hands-on experience with ML pipeline orchestration, CI/CD, and deployment automation. Deep understanding of ML Ops practices, including monitoring, scaling, and retraining More ❯

years experience AWS cloud and AWS services such as Redshift, DMS, RDS, Glue, S3, Athena, Lambda Experience in designing and managing a data warehouse Experience with AWS Bedrock, Sagemaker and PowerBI desirable Strong capability with SQL in both MySQL and Postgresql flavours About You A self-starter looking to make a positive impact in a rapidly evolving environment Ability More ❯

SENIOR DATA ENGINEER PERMANENT CITY OF LONDON £90,000 - £100,000 HYBRID work available - City of London office/Home As a key member of this team, you will play a pivotal role in designing and implementing data warehousing solutions More ❯

SENIOR DATA ENGINEER PERMANENT CITY OF LONDON £90,000 - £100,000 HYBRID work available - City of London office/Home As a key member of this team, you will play a pivotal role in designing and implementing data warehousing solutions More ❯

SENIOR DATA ENGINEER PERMANENT CITY OF LONDON £90,000 - £100,000 HYBRID work available - City of London office/Home As a key member of this team, you will play a pivotal role in designing and implementing data warehousing solutions More ❯

We're partnering with an established AI consultancy seeking a Contract Machine Learning Engineer (NLP) to lead an NLP project for a major utilities client. You'll develop and deploy transformer models, LLMs, and RAG pipelines to extract insights from More ❯

multi-modal models that combine vision and language Strong grasp of data-centric AI practices - annotation tooling, prompt evaluation, and dataset curation Familiarity with MLOps tools (e.g. Weights & Biases, SageMaker, MLflow) Experience working in regulated sectors like insurance, banking, or property What You'll Be Doing This is a hands-on, high-impact role - you'll be building production … image segmentation Apply NLP to real-world business problems - summarisation, entity recognition, information extraction, and more Train and deploy models at scale using AWS - including Lambda, EC2, S3, and SageMaker Meet with enterprise clients to explore problems, present ideas, and share results Prototype fast, test often, and get working solutions into the hands of users Work cross-functionally with More ❯