prompt engineering (e.g., GPT, BERT, T5 family). Familiarity with on-device or edge-AI deployments (e.g., TensorFlow Lite, ONNX, mobile/embedded inference). Knowledge of MLOps tooling (MLflow, Weights & Biases, Kubeflow, or similar) for experiment tracking and model governance. Open-source contributions or published papers in top-tier AI/ML conferences (NeurIPS, ICML, CVPR, ACL, etc.). More ❯
Tools : Experience with monitoring 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. More ❯
of 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 ❯
engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps) Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow) Distributed computing frameworks (e.g., Spark, Dask) Cloud platforms (e.g., AWS, Azure, GCP) and HP computing Containerization and orchestration (Docker, Kubernetes) Strong problem-solving skills and the ability to analyse issues More ❯
Python and associated ML/DS libraries (Scikit-learn, Numpy, LightlGBM, Pandas, LangChain/LangGraph, TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, Athena, etc. MLOps: Terraform, Docker, Airflow, MLFlow More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans, an EV Scheme to further reduce carbon emissions More ❯
Python and associated ML/DS libraries (Scikit-learn, Numpy, LightlGBM, Pandas, LangChain/LangGraph, TensorFlow, etc...) PySpark AWS cloud infrastructure: EMR, ECS, Athena, etc. MLOps: Terraform, Docker, Airflow, MLFlow More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans, an EV Scheme to further reduce carbon emissions More ❯
attention to detail. Nice to Have Experience with ML and/or computer vision frameworks like PyTorch, Numpy or OpenCV. Knowledge of ML model serving infrastructure (TensorFlow Serving, TorchServe, MLflow). Knowledge of WebGL, Canvas API, or other graphics programming technologies. Familiarity with big data technologies (Kafka, Spark, Hadoop) and data engineering practices. Background in computer graphics, media processing, or More ❯
Python and associated ML/DS libraries (scikit-learn, numpy, LightlGBM, Pandas, LangChain/LangGraph TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, Athena, etc. MLOps: Terraform, Docker, Airflow, MLFlow More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans, an EV Scheme to further reduce carbon emissions More ❯
Python and associated ML/DS libraries (scikit-learn, numpy, LightlGBM, Pandas, LangChain/LangGraph, , TensorFlow, etc...) PySpark AWS cloud infrastructure: EMR, ECS, Athena, etc. MLOps: Terraform, Docker, Airflow, MLFlow More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans, an EV Scheme to further reduce carbon emissions More ❯
related B2C environments Strong programming skills in Python, with experience using libraries like scikit-learn, XGBoost, and pandas Practical experience in MLOps or strong knowledge of model deployment (e.g. MLflow, Airflow, Docker, Kubernetes, model monitoring tools) Familiarity with cloud environments (AWS, GCP, or Azure) and data pipelines Excellent communication skills—able to explain technical work to non-technical stakeholders and More ❯
related B2C environments Strong programming skills in Python, with experience using libraries like scikit-learn, XGBoost, and pandas Practical experience in MLOps or strong knowledge of model deployment (e.g. MLflow, Airflow, Docker, Kubernetes, model monitoring tools) Familiarity with cloud environments (AWS, GCP, or Azure) and data pipelines Excellent communication skills—able to explain technical work to non-technical stakeholders and More ❯
Skills: Experience using R and NLP or deep learning techniques (e.g. TF-IDF, word embeddings, CNNs, RNNs). Familiarity with Generative AI and prompt engineering. Experience with Azure Databricks, MLflow, Azure ML services, Docker, Kubernetes. Exposure to Agile development environments and software engineering best practices. Experience working in large or complex organisations or regulated industries. Strong working knowledge of Excel More ❯
Skills: Experience using R and NLP or deep learning techniques (e.g. TF-IDF, word embeddings, CNNs, RNNs). Familiarity with Generative AI and prompt engineering. Experience with Azure Databricks, MLflow, Azure ML services, Docker, Kubernetes. Exposure to Agile development environments and software engineering best practices. Experience working in large or complex organisations or regulated industries. Strong working knowledge of Excel More ❯
exposure to cloud platforms (e.g., AWS, GCP), containerization (Docker, Kubernetes), and scalable data systems (e.g., Spark, Kafka). You are experienced or interested in ML model serving technologies (e.g., MLflow , TensorFlow Serving) and CI/CD tools (e.g., GitHub Actions). You understand ML algorithms and collaborate effectively with cross-functional teams through strong communication skills. Accommodation requests If you More ❯
exposure to cloud platforms (e.g., AWS, GCP), containerization (Docker, Kubernetes), and scalable data systems (e.g., Spark, Kafka). You are experienced or interested in ML model serving technologies (e.g., MLflow , TensorFlow Serving) and CI/CD tools (e.g., GitHub Actions). You understand ML algorithms and collaborate effectively with cross-functional teams through strong communication skills. Accommodation requests If you More ❯
solutions (Docker, Kubernetes) and frameworks like BentoML or equivalent. Familiarity with vector databases and retrieval pipelines for RAG architectures. Knowledge of cloud platforms (AWS, GCP, Azure) and MLOps tooling (MLflow, Kubeflow, or similar). Familiarity with voice-to-text, IVR, and/or computer vision systems is a plus. Strong understanding of software engineering best practices-testing, CI/CD More ❯
TensorFlow) with a strong grounding in evaluating NLP models using classification and ranking metrics, and experience running A/B or offline benchmarks. Proficient with MLOps and training infrastructure (MLflow, Kubeflow, Airflow), including CI/CD, hyperparameter tuning, and model versioning. Strong social media data extraction and scraping skills at scale (Twitter v2, Reddit, Discord, Telegram, Scrapy, Playwright). Experience More ❯
data modeling, data warehousing, data integration, and data governance. Databricks Expertise: They have hands-on experience with the Databricks platform, including its various components such as Spark, Delta Lake, MLflow, and Databricks SQL. They are proficient in using Databricks for various data engineering and data science tasks. Cloud Platform Proficiency: They are familiar with cloud platforms like AWS, Azure, or More ❯
and ETL processes Good knowledge of ML ops principles and best practices to deploy, monitor and maintain machine learning models in production Familiarity with Git CI/CD and MLflow for managing and tracking code deployment or model versions Experience with cloud-based data platforms such as AWS or Google Cloud Platform Nice to have: Experience with Kafka Proven track More ❯
Airflow, Dagster). Proficient in SQL and cloud platforms (AWS preferred), with exposure to model/data versioning tools (e.g. DVC), containerised solutions (Docker, ECS), and experiment tracking (e.g. MLflow). Demonstrated ability to frame business and technical problems, assess trade-offs, and select effective modelling approaches. Excellent communication skills and the ability to influence both technical and non-technical More ❯
into business outcomes and influence stakeholders at all levels. Extra Awesome Experience managing hybrid teams that include both Data Scientists and ML Engineers Exposure to modern MLOps tooling (e.g. MLflow, Feature Store, SageMaker, Vertex AI) Familiarity with unstructured data modeling (e.g. NLP, embeddings, LLMs) and GenAI product patterns Experience working in product-led or B2B SaaS environments You bring a More ❯
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/CD tools and best More ❯
time model serving infrastructure, utilising technologies such as Kafka, Python, Docker, Apache Flink, Airflow, and Databricks. Actively assist in model development and debugging using tools like PyTorch, Scikit-learn, MLFlow, and Pandas, working with models from gradient boosting classifiers to custom GPT-based solutions. gain a deep understanding of Simply Business's wider data ecosystem to build efficient and scalable More ❯
we're looking for 6+ years of experience in software engineering with strong focus on machine learning systems Deep proficiency in Python and ML ecosystem (e.g. PyTorch, scikit-learn, MLFlow) Solid understanding of data and model lifecycle management, versioning, and deployment Experience building ML infrastructure and model-serving pipelines in production environments Familiarity with cloud-based architecture (AWS preferred), containerization More ❯
applied AI 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 More ❯
