1 to 25 of 48 MLflow Jobs in England

in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes). Excellent problem-solving skills and ability More ❯

in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes). Excellent problem-solving skills and ability More ❯

in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes). Excellent problem-solving skills and ability More ❯

in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes). Excellent problem-solving skills and ability More ❯

in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes). Excellent problem-solving skills and ability 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) Ability to scope and effectively deliver projects What we offer More ❯

such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using PySpark. Solid understanding of software engineering principles and version control (e.g., Git). Excellent problem-solving skills and ability More ❯

Azure. Knowledge of vector databases (Azure Cognitive Search, Pinecone, Weaviate, etc.) for LLM applications. Exposure to real time streaming & event driven architectures (Event Hub, Kafka). Familiarity with KubeFlow, MLflow, or other MLOps orchestration frameworks. Personal High analytical skills High customer orientation High quality awareness About Us Infosys is a global leader in next generation digital services and consulting. We More ❯

Build APIs and microservices for scalable AI deployment. Use AI-powered dev tools like GitHub Copilot, Cursor, and Codeium to speed up iteration. Apply MLOps/LLMOps practices with MLflow, Weights & Biases, and Kubeflow. ?? Youll Bring Strong Python skills and experience with LangChain, Transformers, Hugging Face. Solid grasp of LLM behavior, prompt optimization, and data engineering. Familiarity with vector databases More ❯

learn, PyTorch, TensorFlow, and XGBoost. Design robust data pipelines using tools like Spark and Kafka for real-time and batch processing. Manage ML lifecycle with tools such as Databricks , MLflow , and cloud-native platforms (Azure preferred). Collaborate with engineering teams to ensure scalable, secure ML infrastructure aligned with compliance standards (e.g., ISO27001). Ensure data governance, particularly around sensitive 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 ❯

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 ❯

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 ❯

Knowledge of software engineering best practices, version control (Git), or CI/CD pipelines Understanding of deep learning, NLP, or computer vision Familiarity with deployment tools like Docker or MLflow What You’ll Gain Hands-on Experience : Work on real ML systems and contribute to production-grade solutions Mentorship & Training : Learn from senior ML engineers and participate in structured onboarding More ❯

Knowledge of software engineering best practices, version control (Git), or CI/CD pipelines Understanding of deep learning, NLP, or computer vision Familiarity with deployment tools like Docker or MLflow What You’ll Gain Hands-on Experience : Work on real ML systems and contribute to production-grade solutions Mentorship & Training : Learn from senior ML engineers and participate in structured onboarding More ❯

Knowledge of software engineering best practices, version control (Git), or CI/CD pipelines Understanding of deep learning, NLP, or computer vision Familiarity with deployment tools like Docker or MLflow What You’ll Gain Hands-on Experience : Work on real ML systems and contribute to production-grade solutions Mentorship & Training : Learn from senior ML engineers and participate in structured onboarding More ❯

Knowledge of software engineering best practices, version control (Git), or CI/CD pipelines Understanding of deep learning, NLP, or computer vision Familiarity with deployment tools like Docker or MLflow What You’ll Gain Hands-on Experience : Work on real ML systems and contribute to production-grade solutions Mentorship & Training : Learn from senior ML engineers and participate in structured onboarding More ❯

Knowledge of software engineering best practices, version control (Git), or CI/CD pipelines Understanding of deep learning, NLP, or computer vision Familiarity with deployment tools like Docker or MLflow What You’ll Gain Hands-on Experience : Work on real ML systems and contribute to production-grade solutions Mentorship & Training : Learn from senior ML engineers and participate in structured onboarding More ❯

fine-tuning. Proficient in Python, PyTorch or TensorFlow, and scikit-learn. Practical experience deploying ML models in production environments (cloud-native or on-prem). Familiar with data versioning, MLflow, and monitoring frameworks (e.g. Weights & Biases, Evidently). Strong communicator, capable of leading project delivery autonomously. Why Join Step into a key delivery role for a high-value client while More ❯

partners. Nice to have Experience with quality weighted bidding, uplift modelling, or reinforcement style policy optimisation. Familiarity with MMM, MTA, and experiment design in marketing contexts. Vertex AI or MLflow for training and deployment. Containerisation and service reliability skills. Why join us Monthly long weekends: every third Friday off. Wellness stipend and comprehensive parental leave policies. Remote first culture with More ❯

the following technologies : Azure, AWS, GCP Airflow, dbt, Databricks, Snowflake, etc. GitHub, Azure DevOps and related developer tooling and CI/CD platforms, Terraform or other Infra-as-Code MLflow, AzureML or similar for MLOps; LangSmith, Langfuse and similar for LLMOps The difference to our "AI Engineer" role is: Do you "use/consume" these technologies, or are you the More ❯

Implement 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 More ❯

MLOps 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 More ❯