and optimise CI/CD pipelines to automate the deployment of AI applications and models (MLOps/LLMOps). Containerise workloads using Docker and manage deployments through Kubernetes (KubeRay, Kubeflow, MLRun, or similar). Provision and manage the infrastructure required to run AI workloads, including vector databases and hyperscaler AI services. Develop automation scripts in Python to streamline operations and More ❯
systems. Key Responsibilities Build model lifecycle tooling (deployment, monitoring and alerting) for our predictive models (for example claims cost, conversion, retention, market models) Maintain and improve the development environment (Kubeflow) used by our Data Scientists and Actuaries Develop and maintain pricing analytics tools that enable faster impact assessments, reducing manual work Collaborate with the technical pricing, street pricing and product More ❯
engineering principles for building scalable, maintainable, and production-ready AI systems. Proficiency in Python with AI/ML frameworks (PyTorch, TensorFlow). Experience with MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). Proven experience developing applications leveraging More ❯
engineering principles for building scalable, maintainable, and production-ready AI systems. Proficiency in Python with AI/ML frameworks (PyTorch, TensorFlow). Experience with MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). Proven experience developing applications leveraging More ❯
in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven More ❯
in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven More ❯
in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven More ❯
rigorous code quality and testing standards across data science projects Support talent acquisition and continuous learning initiatives Knowledge and Experience Knowledge of ML model development and deployment frameworks (MLFlow, Kubeflow Advanced data querying (SQL) and data engineering pipelines (Airflow Extensive experience with comprehensive unit testing, integration testing, and test coverage strategies Experience working with Product Management teams and ability to More ❯
tuning, and evaluation. Hands-on experience with core GenAI frameworks (e.g., LangChain, LlamaIndex) and Agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph). Proficiency in MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Strong working knowledge and practical deployment experience on major cloud platforms (AWS, Azure, GCP), including their AI/ML services. Strong foundation in software engineering principles for More ❯
tuning, and evaluation. Hands-on experience with core GenAI frameworks (e.g., LangChain, LlamaIndex) and Agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph). Proficiency in MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Strong working knowledge and practical deployment experience on major cloud platforms (AWS, Azure, GCP), including their AI/ML services. Strong foundation in software engineering principles for More ❯
and the ability to influence A drive to learn new technologies and techniques Experience/aptitude towards research and openness to learn new technologies Experience with Azure, Spark (PySpark) Kubeflow - desirable Benefits We pay competitive salaries based on experience of the candidates, along with this, you will be entitled to an award-winning range of benefits including: Access to our More ❯
Knowledge of cutting-edge techniques for Natural Language Processing and Computer Vision Strong grasp of basic probability concepts and machine learning lifecycle Experience with workflow and pipelining frameworks (e.g., Kubeflow, MLFlow, Argo) Understanding and application of Ethical AI considerations Ready to take your career to the next level? Apply today and be part of something extraordinary! Please either apply by More ❯
Solid CI/CD engineering with GitHub Actions and/or Argo CD. Preferred/Bonus - Research experience in edge AI or constrained/offline deployments. - MLOps experience (Sagemaker, Kubeflow, ZenML). - Experience building RESTful services around AI pipelines. - ISO 27001, NIST SSDF, OWASP SAMM, or GDPR compliance literacy. - Experience with AWS Karpenter, Prometheus, or similar observability stacks. Soft Skills More ❯
Solid CI/CD engineering with GitHub Actions and/or Argo CD. Preferred/Bonus - Research experience in edge AI or constrained/offline deployments. - MLOps experience (Sagemaker, Kubeflow, ZenML). - Experience building RESTful services around AI pipelines. - ISO 27001, NIST SSDF, OWASP SAMM, or GDPR compliance literacy. - Experience with AWS Karpenter, Prometheus, or similar observability stacks. Soft Skills More ❯
Knowledge Experience taking models from experiments through to production deployments, with tools such as Docker, Kubernetes & serverless alternatives such as AWS Lambda. Familiarity with MLOps tools such as MLFlow, Kubeflow or Sagemaker. A strong knowledge of cloud platforms (ideally AWS) and their respective services for deploying robust, AI-heavy applications. Bonus Experience Experience with named entity recognition/recommendation systems. More ❯
London, South East, England, United Kingdom Hybrid / WFH Options
Sanderson
Experience: Proven background as a Machine Learning Engineer. Technical Skills: Strong in SQL and Python (Pandas, Scikit-learn, Jupyter, Matplotlib). Data transformation & manipulation : experience with Airflow, DBT and Kubeflow Cloud: Experience with GCP and Vertex AI (developing ML services). Expertise: Solid understanding of computer science fundamentals and time-series forecasting. Machine Learning: Strong grasp of ML and deep More ❯
Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor's or Master's degree in More ❯
Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor's or Master's degree in More ❯
Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor's or Master's degree in More ❯
Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor's or Master's degree in More ❯
Oxfordshire, England, United Kingdom Hybrid / WFH Options
Humand Talent
IaC (Terraform/CloudFormation) Proficiency with containerisation (Docker) and Kubernetes-based environments A passion for experimentation , research, and bridging theory with practice Bonus if you have: MLOps experience (SageMaker, Kubeflow, ZenML) Research or deployments in edge AI or constrained environments Experience building REST APIs around AI systems Familiarity with ISO 27001 , GDPR , or NIST SSDF The Offer Permanent role with More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Luxoft
Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor's or Master's degree in More ❯
convert customer requirements or business challenges into well-defined machine learning solutions We are using many technologies day to day such as various AWS services, GCP, Kubernetes, Ray Serve, Kubeflow, and ReTool. Any experience in these areas would be a bonus Sprout.ai Values Hungry for Growth - Unleash your inner Sprout: Sprouts embrace growth, forget comfort zones, and help Sprout.ai thrive. More ❯
including training, evaluation, and optimisation. Strong grounding in mathematics, statistics, and data analysis. Experience working in Agile environments. Familiarity with technologies such as AWS, GCP, Kubernetes, Ray Serve, and Kubeflow is desirable. ---------------------------------------- Professional Values Growth: Demonstrates curiosity, adaptability, and continuous learning. Accountability: Takes ownership and delivers to a high standard. Innovation: Embraces experimentation and emerging technologies to drive progress. Collaboration More ❯
software engineers upskill on GenAI relevant techniques and tools Technical Requirements 6+ years of development competency across a variety of languages, frameworks, and tooling, such as Python, R, Kubernetes, Kubeflow, JavaScript/JVM, Kafka 4+ years of years of experience in designing, developing, deploying, and monitoring machine learning and GenAI solutions 2+ years of experience in Application development, deployment, and More ❯
