as Scikit-learn, TensorFlow, or PyTorch) Practical experience working with cloud infrastructure (e.g., AWS, Azure, GCP) and a good understanding of architecture, security, and scaling Hands-on experience with Docker and Kubernetes in real-world engineering workflows Solid grasp of ML fundamentals: supervised/unsupervised learning, statistical modelling, evaluation A pragmatic approach to engineering capable of balancing speed, risk, and More ❯
as Scikit-learn, TensorFlow, or PyTorch) Practical experience working with cloud infrastructure (e.g., AWS, Azure, GCP) and a good understanding of architecture, security, and scaling Hands-on experience with Docker and Kubernetes in real-world engineering workflows Solid grasp of ML fundamentals: supervised/unsupervised learning, statistical modelling, evaluation A pragmatic approach to engineering capable of balancing speed, risk, and More ❯
Python using PyTorch/TensorFlow/JAX framework. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker). Algorithms and data structures: Excellent understanding of core CS fundamentals, including common abstract data structures and algorithms with the ability to apply them to optimise production systems. Problem solving More ❯
Experience with big data ecosystems (Kafka, Spark, Flink, Cassandra, Redis, Airflow) Familiarity with cloud platforms (AWS, Azure, GCP) and S3-compatible storage SaaS/PaaS development experience Container technologies (Docker, Kubernetes) Bloomberg is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of age, ancestry, color, gender identity or expression, genetic More ❯
tools/ways of working (e.g. git/GitHub, DevOps tools for deployment) – should be able to show practice of commit early and deploy often Preferred qualifications: Experience with Docker or containerized applications, especially architecture of multi-container applications Demonstrated experience with biological or scientific data (e.g. genomics, transcriptomics, proteomics), or pharmaceutical industry experience Knowledge of AI/ML approaches More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Undisclosed
tools/ways of working (e.g. git/GitHub, DevOps tools for deployment) – should be able to show practice of commit early and deploy often Preferred qualifications: Experience with Docker or containerized applications, especially architecture of multi-container applications Demonstrated experience with biological or scientific data (e.g. genomics, transcriptomics, proteomics), or pharmaceutical industry experience Knowledge of AI/ML approaches More ❯
london, south east england, united kingdom Hybrid / WFH Options
Undisclosed
tools/ways of working (e.g. git/GitHub, DevOps tools for deployment) – should be able to show practice of commit early and deploy often Preferred qualifications: Experience with Docker or containerized applications, especially architecture of multi-container applications Demonstrated experience with biological or scientific data (e.g. genomics, transcriptomics, proteomics), or pharmaceutical industry experience Knowledge of AI/ML approaches More ❯
Manchester Area, United Kingdom Hybrid / WFH Options
djr
within modern web and API environments. Hands-on expertise in C# , Playwright or Cypress , and Postman/SoapUI . Exposure to cloud platforms (Azure or AWS) and containerised environments ( Docker ). Solid understanding of GIT , Agile , and CI/CD workflows. A proactive approach, with the confidence to challenge, mentor, and lead by example. Bonus points for knowledge of database More ❯
customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker). Algorithms and data structures: Excellent understanding of core CS fundamentals, including common abstract data structures and algorithms with the ability to apply them to optimise production systems. Problem solving More ❯
knowledge of CI/CD, automation, and testing frameworks across unit, integration, E2E, and non-functional testing Cloud-native expertise, ideally GCP or Azure (open to AWS) Skilled in Docker, Kubernetes, Helm, Terraform Knowledge of secure development practices , OWASP, authentication/authorisation Familiar with mobile frameworks (e.g. Ionic Capacitor) Strong track record of technical leadership and mentoring across squads Able More ❯
etc.). Exposure to LLMs, embeddings, and vector search APIs . Strong understanding of data engineering , schema design, ETL, and optimisation. Proficiency with cloud (AWS preferred) and containerised deployments (Docker, ECS). Knowledge of secure coding practices and managing sensitive data. Excellent communication, problem-solving, and leadership skills. Nice to have Experience with rerankers (e.g., cross-encoders), hybrid retrieval (SQL More ❯
Proven success in leading cloud migration projects using tools such as AWS Server Migration Service or AWS Database Migration Service. Hands-on experience with DevOps tools (e.g. Git, Jenkins, Docker, Kubernetes). Excellent communication, problem-solving, and time management skills, with the ability to manage multiple priorities in a fast-paced environment. More ❯
Proven success in leading cloud migration projects using tools such as AWS Server Migration Service or AWS Database Migration Service. Hands-on experience with DevOps tools (e.g. Git, Jenkins, Docker, Kubernetes). Excellent communication, problem-solving, and time management skills, with the ability to manage multiple priorities in a fast-paced environment. More ❯
contexts when designing and delivering functionality Tech Stack The engineering team leverages a modern and scalable technology stack: Backend: Python (FastAPI), Node.js Frontend: React, TypeScript Database: PostgreSQL Infrastructure: AWS, Docker, Terraform CI/CD: GitHub Actions, Pulumi Monitoring & Observability: DataDog, Sentry Data & Analytics: dbt, Metabase Internal Tools: Retool Collaboration: Linear, Slack, Notion Candidates are not expected to have experience with More ❯
AWS Serverless and serverless-supported languages DB (ex, Postgresql, CocroachDB) Streaming technologies (Amazon Kinesis, Kafka) Message Queues (SQS, RabbitMQ, ActiveMQ) Experience working within an agile development Experience with containerization (Docker, Kubernetes, etc) Experience with Micro-Service and Service oriented architectures Experience with Core Services around player journeys and experiences Nice to have experience with Sportsbetting and Casino software Nice to More ❯
tracking, CI/CD, containerisation, orchestration, monitoring, model drift. GenAI/LLMs: RAG, prompt engineering, LoRA fine-tuning, safety, evaluation. Tooling: Python, pandas/PySpark, MLflow, Weights and Biases, Docker, Kubernetes, Terraform, GitHub Actions. Cloud: AWS, GCP, Azure. Soft Skills: problem framing, stakeholder communication, technical writing. What you will bring: 3+ years building and deploying ML systems in production Strong More ❯
. Experience implementing machine learning and large language models (LLMs), encompassing deployment, monitoring, and retraining. Familiarity with software engineering guidelines: version control (e.g., Git), CI/CD, containerization (e.g., Docker), and workflow orchestration. Knowledge of cloud platforms and scalable compute environments (Azure preferred). Understanding of data governance, model documentation, and reproducibility in a regulated or research-heavy context. Ability More ❯
of AI design, build, deployment or management Proficiency or certification in Microsoft Office tools, as well as relevant technologies such as Python, TensorFlow, Jupiter Notebook, Spark, Azure Cloud, Git, Docker and/or any other relevant technologies Strong analytical and problem solving skills, with the ability to work on complex projects and deliver actionable insights Exceptional verbal and written communication More ❯
Hands-on experience with cloud platforms (AWS, GCP, or Azure), including large-scale ML infrastructure management. Knowledge of GPU computing for model training and serving. Experience managing containerised workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding More ❯
Hands-on experience with cloud platforms (AWS, GCP, or Azure), including large-scale ML infrastructure management. Knowledge of GPU computing for model training and serving. Experience managing containerised workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Owen Thomas | Pending B Corp™
Hands-on experience with cloud platforms (AWS, GCP, or Azure), including large-scale ML infrastructure management. Knowledge of GPU computing for model training and serving. Experience managing containerised workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding More ❯
East London, London, United Kingdom Hybrid / WFH Options
Owen Thomas | Pending B Corp™
Hands-on experience with cloud platforms (AWS, GCP, or Azure), including large-scale ML infrastructure management. Knowledge of GPU computing for model training and serving. Experience managing containerised workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding More ❯
Hands-on experience with cloud platforms (AWS, GCP, or Azure), including large-scale ML infrastructure management. Knowledge of GPU computing for model training and serving. Experience managing containerised workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding More ❯
