you will be responsible for driving strategy, top line revenue growth and overall end customer adoption of our comprehensive GenAI/ML solutions. AI/ML services such as Amazon Nova, AmazonSageMaker, Amazon Bedrock, and our generative AI offerings. You will lead a senior go-to-market team across 100+ countries in EMEA, leveraging sales … on thinking big, delivering exceptional results for our customers, and working across AWS as to build the future of AI and cloud computing. This position is part of the Amazon Specialist and Partner Organization (ASP). Specialists own the end-to-end go-to-market strategy for their respective technology domains, providing the business and technical expertise to help … candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most More ❯
candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most … and orchestration approaches Hands-on experience with model customization techniques such as fine-tuning, continued pre-training, and LLM-as-judge evaluation Experience with optimization of models on GPUs, Amazon Silicon, or TPUs, also experience with open source frameworks for building applications powered by LLMs like LangChain, LlamaIndex, and/or similar tools Experience building generative AI applications on … AWS using services such as Amazon Bedrock and AmazonSageMakerAmazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of More ❯
job responsibilities The Data Analytics Consultant is an emerging leader with previous Data Analytics experience. The ideal candidate will have experience of design, development and operations using services like Amazon Kinesis, Apache Kafka, Apache Spark, AmazonSageMaker, Amazon EMR. The Data Analytics Consultant is comfortable rolling up their sleeves to design and code modules for infrastructure … candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most … Kinesis, and big data tools such as - Experience developing scalable, high-performance cloud software services. - AWS Certification(s), such as Solutions Architect Associate and/or Data Analytics Specialty. Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We More ❯
Delivery Consultant - AI/ML, Professional Services Job ID: AWS EMEA SARL (UK Branch) The Amazon Web Services Professional Services (ProServe) team is seeking a skilled Delivery Consultant to join our team at Amazon Web Services (AWS). In this role, you'll work closely with customers to design, implement, and manage AWS solutions that meet their technical … business problems 2. Interact with customer directly to understand the business problem, help and aid them in implementation of their ML ecosystem 3. Effectively use Foundation Models available on Amazon Bedrock and AmazonSageMaker to meet our customer's performance needs 4. Analyze and extract relevant information from large amounts of historical data to help automate and … candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most More ❯
ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on experience with MLOps tools (MLflow, SageMaker, Kubeflow, etc.) and version control systems. Strong knowledge of APIs, microservices architecture, and CI/CD pipelines. Proven experience in leading teams, managing stakeholders, and delivering end-to-end More ❯
and logging tools to track system performance and model effectiveness in production environments. Familiarity with MLOps Tools: Knowledge of various MLOps tools and platforms, including MLflow, Databricks, Kubeflow, and SageMaker, to streamline the machine learning lifecycle. Version Control Systems: Proficient in using version control systems such as Git to manage code and collaborate with development teams. Software Testing and More ❯
Strong understanding of statistical analysis, machine learning, and predictive modelling techniques. Proficiency in Python and SQL programming languages. Proficiency with at least one cloud-based ML platform, such as SageMaker, Vertex AI or Azure Machine Learning Studio. Good knowledge of DevOps practices and tools (e.g.: Git, Docker, Kubernetes). Familiarity with AI platforms and frameworks such as LangChain, Llamaindex More ❯
potential iterations which may drive improved model reliability and accuracy. Develop a testing framework and test model quality Experience with deployment to production, using standard tools including GitHub, Jenkins, Sagemaker, Docker etc Advanced programming skills in SQL, SAS (desired), Python, and the ability to write production-level code Familiarity with the most standard Python libraries used in ML (e.g. More ❯
for data insights Data Bricks/Data QISQL for data access and processing (PostgreSQL preferred, but general SQL knowledge is important) Latest Data Science platforms (e.g., Databricks, Dataiku, AzureML, SageMaker) and frameworks (e.g., TensorFlow, MXNet, scikit-learn) Software engineering practices (coding standards, unit testing, version control, code review) Hadoop distributions (Cloudera, Hortonworks), NoSQL databases (Neo4j, Elastic), streaming technologies (Spark More ❯
/CD, and API design. Hands-on experience with at least one major cloud platform (AWS, Azure, GCP) in a production setting, and familiarity with ML platforms like AWS Sagemaker or Azure Machine Learning Studio. A commitment to continuous learning and skill development, and the ability to foster growth in others. We recruit individuals at all levels based on More ❯
class classification, decision trees, support vector machines, and neural networks (deep learning experience strongly preferred) Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services , AWS Bedrock, S3, SageMaker; Azure AI Search, OpenAI, blob storage, etc. Bachelors degree (masters or higher strongly preferred) in machine learning, computer science, data science, applied mathematics or related technical field What Would More ❯
delivery Proven track record deploying ML systems in production at scale (batch and/or real-time) Strong technical background in Python and ML engineering tooling (e.g. MLflow, Airflow, SageMaker, Vertex AI, Databricks) Understanding of infrastructure-as-code and CI/CD for ML systems (e.g. Terraform, GitHub Actions, ArgoCD) Ability to lead delivery in agile environmentsbalancing scope, prioritisation More ❯
and Python, understanding of data structures, algorithms, and software design patterns. Experience with AI/Gen AI frameworks like TensorFlow or PyTorch. Experience with cloud platforms such as AWS SageMaker or Azure Machine Learning. Ability to translate business problems into solutions. Strong communication skills; bilingualism/multilingualism is a plus. Willingness to travel (50% - national and international). Our More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Omnis Partners
AI assistants and agentic tools to maximise impact. Bonus points if you’ve played with: LangGraph, AutoGen, or other agentic AI frameworks LoRA fine-tuning, RLHF, or vLLM AWS (SageMaker, EKS), Docker, or speech-to-text systems 🌟 What You Get: 100% remote role (UK or Spain) Work on real-world LLM deployments in an agile, experimental environment More ❯
AI assistants and agentic tools to maximise impact. Bonus points if you’ve played with: LangGraph, AutoGen, or other agentic AI frameworks LoRA fine-tuning, RLHF, or vLLM AWS (SageMaker, EKS), Docker, or speech-to-text systems 🌟 What You Get: 100% remote role (UK or Spain) Work on real-world LLM deployments in an agile, experimental environment More ❯
Python Experience with machine learning, familiar with Huggingface, Pytorch, and similar ML tools and packages Familiarity with deploying and scaling ML models in the cloud, particularly with AWS and SageMaker Understanding of DevOps processes and tools: CI/CD, Docker, Terraform, and monitoring/observability Bonus: experience with vector databases, semantic search, or event-driven systems like Kafka Additional More ❯
wed like to see from you: Extensive experience designing and deploying ML systems in production Deep technical expertise in Python and modern ML tooling (e.g. MLflow, TFX, Airflow, Kubeflow, SageMaker, Vertex AI) Experience with infrastructure-as-code and CI/CD practices for ML (e.g. Terraform, GitHub Actions, ArgoCD) Proven ability to build reusable tooling, scalable services, and resilient More ❯
experience in AI/LLM systems Strong proficiency with Python, and familiarity with Scala, Go, or Rust Cloud platform expertise with AWS, GCP, or Azure, including AI-specific services (SageMaker, Vertex AI, Azure AI) Databricks platform experience with Unity Catalog, MLflow, and Databricks Machine Learning for end-to-end AI/ML workflows Modern ML infrastructure experience with tools More ❯
Agile, and test-driven development. Docker/Kubernetes containerisation and CI/CD pipelines. Additional valued skills include: AWS implementation experience using IaC and best practices (EC2, EKS, GPUs, SageMaker, RDS). Curious engineers with good collaborative skills. Data engineering experience with frameworks like Airflow, DBT, Spark. Bachelor's degree in Computer Science, related fields, or equivalent experience. This More ❯
independently Excellent communication skills across technical and non-technical stakeholders Experience designing systems in modern cloud environments (e.g. AWS, GCP) Technologies and Tools Python ML and MLOps tooling (e.g. SageMaker, Databricks, TFServing, MLflow) Common ML libraries (e.g. scikit-learn, PyTorch, TensorFlow) Spark and Databricks AWS services (e.g. IAM, S3, Redis, ECS) Shell scripting and related developer tooling CI/ More ❯
deploying ML models into production environments Proficiency in designing scalable data pipelines and real-time inference systems Understanding of modern ML tooling and frameworks (e.g., PyTorch, TensorFlow, MLflow, AWS SageMaker) Strong cross-functional collaboration skills, particularly with data science and product teams Clear communication and an ability to prioritize for both experimentation and reliability Bonus Familiarity with optimization, time More ❯
deploying ML models into production environments Proficiency in designing scalable data pipelines and real-time inference systems Understanding of modern ML tooling and frameworks (e.g., PyTorch, TensorFlow, MLflow, AWS SageMaker) Strong cross-functional collaboration skills, particularly with data science and product teams Clear communication and an ability to prioritize for both experimentation and reliability Bonus Familiarity with optimization, time More ❯
Classification, Clustering, Decision Trees, SVMs, Neural Networks) Model Evaluation and Validation Big Data Technologies (e.g., Spark, Hadoop - conceptual understanding) Database Querying (e.g., SQL) Cloud-based Data Platforms (e.g., AWS Sagemaker, Google AI Platform, Azure ML) Ethics in Data Science and AI Person Specification: Experience supporting data science training or mentoring professionals in a data-focused capacity Strong ability to More ❯
direction across multiple teams. • Extensive experience in large scale machine learning, including building, deploying, scaling and securing ML infrastructure in cloud-native environments. • Strong experience with AWS services including SageMaker, Bedrock, S3, EC2, Lambda, IAM, VPC, ECS/EKS, DynamoDB, Kafka, CloudFormation and associated technologies such as Python • Proven ability to drive cross functional technical initiatives and deliver results More ❯
