20 of 20 Amazon SageMaker Jobs in the South East

scikit-learn, Hugging Face, etc.). • Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). • Experience designing scalable ML infrastructure on cloud platforms (AWS SageMaker, GCP AI Platform, Azure ML, or equivalent). • Solid understanding of data-engineering concepts: SQL/noSQL, data pipelines (Airflow, Prefect, or similar), and batch/streaming frameworks (Spark More ❯

About Apexon: Apexon brings together distinct core competencies – in AI, analytics, app development, cloud, commerce, CX, data, DevOps, IoT, mobile, quality engineering and UX, and our deep expertise in BFSI, healthcare, and life sciences – to help businesses capitalize on the More ❯

within large organisations, through e.g. the RFI/RFP process, as preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms – demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning More ❯

developing and deploying data-driven solutions, with a particular focus on OCR use-cases and LLM applications within AWS environments. Key Responsibilities: - AWS Data Science Tools: Hands-on with SageMaker, Lambda, Step Functions, S3, Athena. - OCR Development: Experience with Amazon Textract, Tesseract, and LLM-based OCR. - Python Expertise: Skilled in Pandas, NumPy, scikit-learn, PyTorch, Hugging Face Transformers … GDPR), PII handling. - Agile Working: Experience in Agile/Scrum teams (Jira, Azure DevOps). Essential Skills & Experience: - 5-7 years in a Data Science role - Strong experience with Amazon Bedrock and SageMaker - Python integration with APIs (e.g., ChatGPT) - Demonstrable experience with LLMs in AWS - Proven delivery of OCR and document parsing pipelines More ❯

and development to succeed. A progressive organisation where you can really make a difference. We a great opportunity for a Data Scientist. You will have solid experience of Bedrock, Sagemaker, Python and Pandas. Key Responsibilities: * Working experience LLMs under Bedrock and Sagemaker both Amazon * Python with APIs to ChatGPT * Strong coding skills using libraries like pandas, NumPy … Experience: AWS Data Science Environment: Hands-on experience with Sage Maker, Lambda, Step Functions, S3, Athena. Model deployment and pipeline orchestration in AWS. OCR Use-Case Development: Proficiency with Amazon Tex-tract, Tesseract, and LLM-based OCR. Building document parsing pipelines, validations, and rule Python Proficiency: Strong coding skills using libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging … actionable insights. Stakeholder Collaboration: Effective communication with technical and non-technical stakeholders. Experience in cross-functional team. Data Engineering Basics: Familiarity with SQL and big data processing tools (e.g., Amazon Athena). Model Evaluation & Experimentation: A/B testing, statistical analysis, and performance metrics. Security & Compliance Awareness: Understanding of data privacy, PII handling, and compliance standards (e.g., GDPR). More ❯

and development to succeed. A progressive organisation where you can really make a difference. We a great opportunity for a Data Scientist. You will have solid experience of Bedrock, Sagemaker, Python and Pandas. Key Responsibilities: * Working experience LLMs under Bedrock and Sagemaker both Amazon * Python with APIs to ChatGPT * Strong coding skills using libraries like pandas, NumPy … Experience: AWS Data Science Environment: Hands-on experience with Sage Maker, Lambda, Step Functions, S3, Athena. Model deployment and pipeline orchestration in AWS. OCR Use-Case Development: Proficiency with Amazon Tex-tract, Tesseract, and LLM-based OCR. Building document parsing pipelines, validations, and rule Python Proficiency: Strong coding skills using libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging … actionable insights. Stakeholder Collaboration: Effective communication with technical and non-technical stakeholders. Experience in cross-functional team. Data Engineering Basics: Familiarity with SQL and big data processing tools (e.g., Amazon Athena). Model Evaluation & Experimentation: A/B testing, statistical analysis, and performance metrics. Security & Compliance Awareness: Understanding of data privacy, PII handling, and compliance standards (e.g., GDPR). More ❯

balance cost, speed and data quality. Experimentation - set up offline metrics and online A/B tests; analyse uplift and iterate quickly. Production delivery - build scalable pipelines in AWS SageMaker (moving to Azure ML); containerise code and hook into CI/CD. Monitoring & tuning - track drift, response quality and spend; implement automated retraining triggers. Collaboration - work with Data Engineering … Data Factory) job. Review: inspect dashboards, compare control vs. treatment, plan next experiment. Tech stack Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow) SQL (Redshift, Snowflake or similar) AWS SageMaker → Azure ML migration, with Docker, Git, Terraform, Airflow/ADF Optional extras: Spark, Databricks, Kubernetes. What you'll bring 3-5+ years building optimisation or recommendation systems at … know-how - LP/MIP, heuristics, constraint tuning, objective-function design. Python toolbox: pandas, NumPy, scikit-learn, PyTorch/TensorFlow; clean, tested code. Cloud ML: hands-on with AWS SageMaker plus exposure to Azure ML; Docker, Git, CI/CD, Terraform. SQL mastery for heavy-duty data wrangling and feature engineering. Experimentation chops - offline metrics, online A/B More ❯

of cloud-native architectures (AWS preferred). Experience leading technical teams and mentoring engineers. Excellent stakeholder management and communication skills. Preferred Qualifications: Familiarity with AWS AI services (e.g. Bedrock, SageMaker, Lambda, Step Functions). Experience with low/no-code GenAI tools. Background in API design, platform development, or DevOps for AI systems. Exposure to ethical AI and data More ❯

track record of delivering projects using agile methodologies (Scrum, Kanban, etc.) Nice to have Hands-on experience with AI and Machine Learning , including model development, deployment, and inference using Amazon SageMaker and Amazon Bedrock Benefits: Annual Bonus, 30 days holiday + bank holidays, Private Healthcare, Life Assurance + much more. Please feel free to reach me on More ❯

between agents, models and services Lead implementation of full-stack interfaces and services using Node.js, TypeScript, React.js, Express, Next.js Integrate ML models and embeddings into production pipelines using AWS SageMaker , Bedrock or OpenAI APIs Build support systems for autonomous agents including memory storage, vector search (e.g., Pinecone, Weaviate) and tool registries Enforce system-level requirements for security, compliance, observability … fail-safe operations Must-Have Skills 5+ years in enterprise solution architecture with emphasis on AI/ML system design Deep hands-on experience with AWS : Lambda, API Gateway, SageMaker, DynamoDB, ECS, Step Functions Expertise in JavaScript/TypeScript ecosystem (Node.js, React.js, Express, Next.js) Strong understanding of LLM integration , prompt chaining and tools like LangChain or Semantic Kernel Experience More ❯

performing data science teams. Excellent stakeholder management and communication skills – able to explain complex concepts in accessible language. Proficiency in tools such as Azure ML Studio , Databricks , AWS/SageMaker , Snowflake , and cloud-native platforms. Familiarity with CI/CD tools like Azure DevOps Pipelines or GitHub Actions . Comfortable working in Agile environments and contributing to iterative product More ❯

DevOps teams to design, deploy, and maintain scalable machine learning systems in production. Key Responsibilities: Design, develop, and deploy machine learning models in production environments using AWS services (e.g., SageMaker, Lambda, EC2, S3, EKS, etc.). Build scalable data pipelines for training and inference using AWS Glue, Step Functions, and other ETL tools. Integrate ML models into existing systems More ❯

collaboratively across disciplines. ● Experience integrating or leveraging Large Language Models (LLMs) via APIs or platform services (e.g., OpenAI, Anthropic, Cohere). ● Familiarity with MLOps tools and practices (e.g., MLflow, SageMaker, Kubeflow). ● Experience in startup or fast-paced environments with rapidly evolving requirements Personal qualities ● Ambitious ● Driven ● Ability to work as part of a collaborative team but also work More ❯

systems Ideal Background: Hands-on experience building and maintaining machine learning models in production Strong skills in Python , SQL , and modern cloud environments (AWS preferred) Experience with services like SageMaker , Lambda , or real-time processing tools Understanding of MLOps , versioning, and model monitoring best practices Practical mindset with the ability to balance technical depth with speed of delivery Bonus More ❯

Desirable experience: Familiar with Terraform, from a developer perspective. Previous work in financial services, healthcare, or similarly regulated data environments. Experience with LLMs, voice agents, or tools like AWS SageMaker, AWS Bedrock, or ElevenLabs. Exposure to soft real-time systems or distributed system architectures. Knowledge of OAuth, SQS, or running containers in production. More ❯

data science teams. Ability to build and communicate complex solutions to stakeholders across different levels and disciplines. Proficiency working with modern cloud-based tools (e.g., Azure ML , Databricks , Snowflake , SageMaker , etc.). Deep knowledge of machine learning techniques including predictive modelling, pattern recognition, and optimisation. Strong stakeholder management and product ownership skills. Experience with CI/CD tools such More ❯

in Data Science & ML Expert in Python , data wrangling, EDA, modelling, validation, and deployment Strong in supervised & unsupervised learning ; exposure to NLP/OCR a plus Proficient with AWS SageMaker , Databricks ; Palantir Foundry/AIP is a big plus Insurance/underwriting/claims background is a bonus Confident communicator with client-facing experience Interview Process: Round 1: Technical More ❯

in a consultancy, pre-sales, or technical advisory role. Knowledge and experience of one or more of the following key data technologies; Azure Fabric, SQL Server, Databricks, Snowflake, AWS Amazon Sagemaker, Oracle or equivalents. Proposal & Communication Expertise Strong written and verbal communication skills, with a proven ability to articulate complex concepts to technical and business audiences. Experience of More ❯

assessment in insurance, particularly in underwriting and claims. Apply natural language processing (NLP) for tasks like document classification and information extraction using OCR. Utilize cloud-based platforms like AWS SageMaker and Databricks for scalable model training and deployment. My client is providing access to; Hybrid 2 days (London), 28 Days Holiday, Plus Bank Holiday Private Medical Health Pension Scheme More ❯

data extraction ● Deploying models using cloud-based platforms for scalability Key skills we're looking for: ● Strong Python expertise ● Hands-on experience with statistical modelling techniques ● Familiarity with AWS SageMaker, Databricks, and similar tools ● Proficiency in NLP techniques (e.g., text extraction, classification) ● Prior experience deploying predictive models in the insurance domain If this sounds like you, or someone you More ❯