1 to 25 of 56 MLflow Jobs in the UK

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 ❯

including fine-tuning, RLHF, parameter-efficient methods (LoRA/QLoRA), or custom post-training workflows MLOps experience : Knowledge and familiarity with MLOps frameworks and tools such as Sagemaker, Kedro, MLflow or Weights and Biases Energy Domain Knowledge: Background in power systems, energy dispatch optimisation, grid modelling, or other energy sector applications where AI/ML drives operational decisions Our Tech 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 ❯

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 ❯

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 ❯

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 ❯

with security tools for SAST, DAST, SCA, and secrets management (e.g., HashiCorp Vault). MLOps & AI/ML Knowledge: Must have practical experience with MLOps tools and workflows (e.g., MLflow, Kubeflow, Seldon Core) and an understanding of the ML lifecycle. o Understanding of the unique security vulnerabilities associated with AI/ML systems. Programming & Scripting: Strong scripting skills in Python 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 ❯

regulated environments. • Collaborate closely with data scientists and software engineers to productionize prototypes into scalable, maintainable solutions. Essential Skills & Experienc: • Hands-on experience with ML Ops tools such as MLflow, Kubeflow, Amazon SageMaker, Vertex AI, orequivalent platforms. • Deep understanding of cloud infrastructure services (AWS, Azure, GCP). • Strong experience with CI/CD practices and containerization tools (Docker, Kubernetes). More ❯