10 of 10 MLflow Jobs in Central London

etc.) Experience with LLMs/GenAI frameworks (LangChain, RAG, prompt engineering, etc.) Experience in cloud (AWS/Azure/GCP) Experience in MLOps tools (MLflow, Kubeflow, SageMaker, Azure ML, etc.) API development experience ...

services (e.g. S3, Lambda, SageMaker) Understanding of core ML concepts: model evaluation, validation, and experimentation Familiarity with MLOps or experiment tracking tools (e.g. MLflow, Weights & Biases) Awareness of LLMs or generative AI concepts Strong problem-solving ability and willingness to learn in a fast-paced environment Ability to communicate technical ...

XGBoost, LightGBM, or CatBoost. Experience with model deployment tools (e.g., ONNX, TensorRT, TensorFlow Serving, TorchServe). Familiarity with ML lifecycle tools such as MLflow, Kubeflow, or Azure ML Pipelines. Experience working with distributed data processing (e.g., PySpark) and SQL. Understanding of software engineering best practices, including version control (Git). ...

learn, XGBoost, PyTorch, or TensorFlow Strong experience with cloud-based ML deployment (e.g., AWS SageMaker, Lambda, S3) Knowledge of MLOps/LLMOps tooling (e.g., MLflow, Weights & Biases, Data Version Control) Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures Ability to absorb complex scientific and technical concepts ...

teams and delivering enterprise-grade data solutions. Preferred Qualifications Databricks certifications (e.g., Databricks Certified Data Engineer Professional, Architect ). Experience building ML solutions using MLflow or integrating with cloud ML services. Experience in BFSI, Retail, Telecom, or Healthcare data environments. Experience with real-time data processing (Kafka, EventHub, Kinesis). ...

building simple data pipelines or transformation workflows is a plus. Exposure to ML Ops or production systems (nice to have) Familiarity with tools like MLflow, Weights & Biases, or cloud platforms (Azure, AWS, or GCP). Experience deploying models via APIs or lightweight services is a bonus, not a requirement. Software ...

Python (expert), C++ for inference (strong — Android NDK), Swift and Kotlin awareness (basic — enough to understand integration surface), Git/CI/CD, Docker, MLflow/W&B. Required Skills Expertise in machine learning frameworks, computer vision, signal processing, model optimisation, and programming languages as detailed above. Preferred Skills Experience ...

Senior ML/MLOps Engineer (MLflow) | Databricks | Azure) About Optima Partners We’re an advanced data & business consultancy helping major consumer brands transform through data-driven customer strategy, design, and engineering. Our clients include leading UK and global brands across financial services, retail, and more. Join a practitioner-led, fast … loves building real, production grade ML systems not just proof‐of‐concepts. If you enjoy working across the full ML lifecycle and have strong MLflow experience, this role is built for you. What You’ll Do Own end-to-end ML model lifecycles from training to deployment to monitoring Productionise ...

models. The focus of the role is building and enabling production-grade ML environments rather than model development itself. Candidates must have deep MLflow experience and proven delivery in real-world client settings. Key Responsibilities Design, build, and maintain end-to-end MLOps environments to support model training, tracking, deployment … monitoring Implement MLflow for: Experiment tracking; Model registry; Model versioning and lifecycle management Enable model deployment into production (batch and/or real-time) with robust CI/CD Work closely with Data Scientists to transition models from experimentation to production Build scalable, secure, and reproducible ML platforms Establish best ...

turn ideas into impactful solutions. Optimize and deploy models with tools like TensorFlow Serving, TorchServe, ONNX, and TensorRT. Build and manage ML pipelines using MLflow, Kubeflow, and Azure ML Pipelines. Work with large-scale data using PySpark and integrate ML solutions into production environments. Monitor and improve model performance … LightGBM, CatBoost, TensorFlow, Keras, PyTorch). Familiarity with model deployment and serving tools (ONNX, TensorRT, TensorFlow Serving, TorchServe). Experience with ML lifecycle tools (MLflow, Kubeflow, Azure ML Pipelines). Knowledge of distributed data processing (PySpark) and software engineering principles (Git). A collaborative mindset and excellent problem-solving abilities. ...