1 to 25 of 61 Remote MLflow Jobs in the UK

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 ❯

warehousing solutions (Snowflake, BigQuery, Redshift) Experience with cloud platforms (AWS, Azure, GCP) and their ML and AI services (SageMaker, Azure ML, Vertex AI) Knowledge of MLOps tools including Docker, MLflow, Kubeflow, or similar platforms Experience with version control (Git) and collaborative development practices Excellent analytical thinking and problem-solving abilities Strong communication skills with ability to explain technical concepts to More ❯

with PyTorch, TensorFlow, Scikit-learn, and transformer-based models. Practical knowledge of LLM integration (e.g., GPT, Claude) and RAG architecture. Experience with ML lifecycle management tools like AWS SageMaker, MLflow, or Databricks. Working knowledge of CUDA, Nvidia GPUs, and distributed training. Experience with AWS services (S3, Lambda, EC2, SageMaker, Bedrock, etc.). Desired: Experience deploying models as APIs/microservices More ❯

years experience in AI/ML roles or relevant hands-on projects Nice to Have (Bonus) : NLP, Computer Vision, or Reinforcement Learning experience Knowledge of MLOps tools (MLflow, Kubeflow, etc.) Familiarity with SQL or Big Data tools (e.g., Spark) ️ Please apply only if you meet the skill and experience criteria. This role is open and hiring now don't delay. More ❯

or Azure) Ability to work independently and communicate effectively in a remote team Bonus Points Experience with Hugging Face Transformers , LangChain , or RAG pipelines Knowledge of MLOps tools (e.g., MLflow, Weights & Biases, Docker, Kubernetes) Exposure to data engineering or DevOps practices Contributions to open-source AI projects or research publications What We Offer Fully remote working A collaborative and inclusive More ❯

or Azure) Ability to work independently and communicate effectively in a remote team Bonus Points Experience with Hugging Face Transformers , LangChain , or RAG pipelines Knowledge of MLOps tools (e.g. MLflow, Weights & Biases, Docker, Kubernetes) Exposure to data engineering or DevOps practices Contributions to open-source AI projects or research publications What We Offer Fully remote working A collaborative and inclusive More ❯

Libraries: LangChain, Hugging Face Transformers, PyTorch, TensorFlow, Scikit-learn Agentic AI Tools: OpenAI GPT models, Crew,AI, Cohere, Pinecone (for vector databases), AutoGPT Data Engineering & ML Pipelines: Apache Airflow, MLflow, Kubeflow, dbt, Prefect Cloud & Deployment Platforms: AWS SageMaker, Azure ML, Google Vertex AI APIs & Orchestration: OpenAI API, Anthropic Claude, Weaviate, FastAPI (for AI applications) MLOps & Experimentation: Weights & Biases, DVC (Data More ❯

design discussions, and performance tuning Requirements: 3+ years of experience in a Machine Learning Engineer or similar role Proficiency in Python , ML frameworks (TensorFlow, PyTorch), and deployment tools (Docker, MLflow, etc.) Experience building scalable ML pipelines in cloud environments (AWS, GCP or Azure) Familiarity with energy systems, smart metering, or IoT data is a significant bonus Bachelors or Masters degree 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 ❯

and data teams Optimise model performance for accuracy, speed, and cost Run experiments, A/B tests, and validate approaches statistically Use tools like Python, TensorFlow, PyTorch, Scikit-learn, MLflow, SQL, Docker, AWS/GCP What Youll Need: Bachelors degree in Computer Science, AI, Machine Learning, or related field 1+ year of experience in a Machine Learning Engineer or similar More ❯

and data teams Optimise model performance for accuracy, speed, and cost Run experiments, A/B tests, and validate approaches statistically Use tools like Python, TensorFlow, PyTorch, Scikit-learn, MLflow, SQL, Docker, AWS/GCP What Youll Need: Bachelors degree in Computer Science, AI, Machine Learning, or related field 1+ year of experience in a Machine Learning Engineer or similar 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 ❯

workflows, containerisation (e.g., Docker, Kubernetes) and production-grade APIs Understanding of data governance, privacy and regulatory compliance (e.g., GDPR) Nice to have: Familiarity with Infrastructure as Code (e.g., Ansible), MLFlow, or orchestration frameworks Background in both object-oriented and functional programming paradigms Please note: Visa sponsorship is unfortunately not available for this role. Applicants must have the right to work More ❯

foundational LLMs to custom built cognitive agents, solving real world business challenges. Establishing scalable ML/AI pipelines and infrastructure, working closely with data engineering and DevOps functions (Kubernetes, MLflow, Vertex AI, etc.). Acting as a thought leader, identifying new AI use cases and communicating technical vision to executive stakeholders and cross-functional teams. Champion ethical AI principles, ensuring 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 ❯

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 ❯

with large-scale and sophisticated data sets Experience in applying machine learning to domains such as e-commerce, finance, health care, etc. Experience in using ML tools such as Mlflow for model lifecycle management. Experience in building ML models in production using AWS ecosystem, especially ML related services such as SageMaker. Familiarity with large language models (LLM). Proficiency in More ❯

a related discipline, with a BSc required and an MSc considered advantageous. Experienced in using machine learning frameworks such as Scikit-learn, Keras, and PyTorch, with additional familiarity with MLFlow and AzureML seen as a positive. Have working knowledge of CI/CD practices, ML Ops, ML pipelines, automated testing, and platforms such as AzureML, Google Cloud, or AWS. Possess More ❯

/CD tools and pipelines for data science Solid understanding of AWS services (e.g. EC2, S3, Lambda, Glue) and CDK Proficient in Python and PySpark; SQL fluency Experience with MLflow or other model lifecycle tools Effective communicator and trainer - able to help others upskill Comfortable building internal tools and documentation Nice to Have: Experience with Terraform, dbt, or Great Expectations More ❯

on-premise and cloud environments to handle text and audio data processing loads for ML models Deploy NLP models in cloud environments (AWS SageMaker) through Jenkins Design and implement MLflow and other ML Ops applications to streamline ML workflows which adhere to strict data privacy and residency guidelines Communicate your work throughout the team and related departments Mentor and guide More ❯

of or monitoring, retraining, and lifecycle management for deployed AI/ML systems, ensuring continued accuracy, fairness, and compliance. Familiarity with experiment tracking tools and automated ML workflows (e.g., MLflow). Experience and skills we'd love: We've included some further skills and experience that would be great. However, don't rule yourself out if you haven't had More ❯

learn, TensorFlow, PyTorch, or Hugging Face Solid understanding of machine learning fundamentals and performance evaluation techniques Experience working in cloud platforms (AWS, GCP, or Azure) with MLOps tools (e.g. MLflow, SageMaker, Vertex AI) Comfortable working independently and delivering high-quality work to tight timelines Experience working in fast-paced environments or scale-up settings Company Market leading financial services (fintech More ❯

ML Engineering culture What wed like to see from you: Extensive experience designing and deploying ML systems in production Deep technical expertise in Python and modern ML tooling (e.g. MLflow, TFX, Airflow, Kubeflow, SageMaker, Vertex AI) Experience with infrastructure-as-code and CI/CD practices for ML (e.g. Terraform, GitHub Actions, ArgoCD) Proven ability to build reusable tooling, scalable More ❯

ML Engineering culture What wed like to see from you: Extensive experience designing and deploying ML systems in production Deep technical expertise in Python and modern ML tooling (e.g. MLflow, TFX, Airflow, Kubeflow, SageMaker, Vertex AI) Experience with infrastructure-as-code and CI/CD practices for ML (e.g. Terraform, GitHub Actions, ArgoCD) Proven ability to build reusable tooling, scalable More ❯

but not essential NLP/Deep learning experience (e.g. huggingface, spaCy) Deep learning framework experience (preferably PyTorch) MLOps experience (e.g. data and model versioning, model deployment CI/CD, MLFlow/DVC) Cloud platform experience, especially from an ML standpoint (AWS preferred) Statistical testing experience Experience with AWS Bedrock Experience with C# Containerization via Docker. Awareness of basic data science More ❯