1 to 25 of 40 Remote MLflow Jobs in London

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 ❯

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 ❯

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 ❯

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 ❯

Frameworks: PyTorch, TensorFlow, Hugging Face Transformers Bio-AI Tools: AlphaFold, RoseTTAFold, BioBERT, DeepCell, Cellpose Data Sources: Genomic datasets, microscopy images, biomedical literature Cloud & DevOps: AWS/GCP, Docker, Kubernetes, MLflow Languages: Python (essential), SQL, Bash Ideal Candidate Profile: 4+ years of experience in machine learning, with at least 2 years in biotech, healthcare, or life sciences Strong understanding of biological 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 ❯

Python and scripting for automation Solid understanding of cloud networking, security, and cross-cloud connectivity Experience in monitoring, observability, and cost optimisation Nice to Have Experience with ML tooling (MLflow, Kubeflow) Knowledge of FastAPI , Databricks, or Snowflake Exposure to SRE practices or cloud security certifications Familiarity with Prometheus , Grafana , or Datadog Interested? If you want to be part of a 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 ❯

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 ❯

LLMs - fine-tuning, prompt engineering, vector databases, or RAG pipelines Proven experience with A/B testing, experimentation design, or causal inference to guide product decisions Exposure to Databricks, MLflow, AWS, and PySpark (or similar technologies) is a plus Excitement about Ophelos' mission to support households and businesses in breaking the vicious debt cycle About Our Team Ophelos launched in More ❯

including APIs, data structures, and async processing Databricks/Microsoft Fabric Cloud, preferably Azure (Data Lake, Functions, App Services) Containerisation with Docker and CI/CD pipelines MLOps tooling (MLFlow, Git-based versioning, environment tracking) Desirable Skills & Interests LangChain, Langflow, or similar frameworks for building AI agents LLMs or intelligent automation workflows High-availability, scalable systems (microservices, event- based architectures More ❯

including APIs, data structures, and async processing * Databricks/Microsoft Fabric * Cloud, preferably Azure (Data Lake, Functions, App Services) * Containerisation with Docker and CI/CD pipelines * MLOps tooling (MLFlow, Git-based versioning, environment tracking) Desirable Skills & Interests * LangChain, Langflow, or similar frameworks for building AI agents * LLMs or intelligent automation workflows * High-availability, scalable systems (microservices, event- based architectures More ❯

including APIs, data structures, and async processing* Databricks/Microsoft Fabric* Cloud, preferably Azure (Data Lake, Functions, App Services)* Containerisation with Docker and CI/CD pipelines* MLOps tooling (MLFlow, Git-based versioning, environment tracking)Desirable Skills & Interests* LangChain, Langflow, or similar frameworks for building AI agents* LLMs or intelligent automation workflows* High-availability, scalable systems (microservices, event-based architectures More ❯

and platform engineering teams to identify technical gaps, scope backend development efforts, and deliver core platform capabilities such as: Model training orchestration. CI/CD for ML (e.g., using MLflow, Kubeflow, or Vertex AI Pipelines). Model versioning, monitoring, and governance. Enable high-impact AdTech use cases including: Marketing Mix Modelling (MMM). Real-time personalisation and bidding. Audience segmentation More ❯

ll take full ownership of our AI infrastructure, designing and scaling platforms that power real-world solutions. Working hands-on with technologies like Azure AI Foundry, Kubernetes, Terraform, and MLflow, you'll collaborate with architects, data scientists, engineers, and business stakeholders to ensure our platforms meet evolving needs. As a leader, you'll grow and guide a team of platform More ❯

native environments Strong technical background in data engineering, analytics, or science Experience working with Finance and Risk teams Familiar with AWS and modern data/AI stacks (SQL, Python, MLflow, MLOps) Strategic mindset with ability to scale teams and influence at C-level More ❯

solutions using cutting-edge tools like LangGraph, FastAPI, and HuggingFace. Own the full AI product lifecycle - from idea to production. Architect intelligent systems using Python, microservices, and MLOps tooling (MLflow, DVC). Work closely with cross-functional teams to turn real-world problems into working software. 🛠️ What You Bring: Fluency in Python and strong grasp of ML/DL concepts. More ❯

solutions using cutting-edge tools like LangGraph, FastAPI, and HuggingFace. Own the full AI product lifecycle - from idea to production. Architect intelligent systems using Python, microservices, and MLOps tooling (MLflow, DVC). Work closely with cross-functional teams to turn real-world problems into working software. 🛠️ What You Bring: Fluency in Python and strong grasp of ML/DL concepts. More ❯

command of the English language. Nice-to-Have Alongside the essential prerequisites, we're excited to find experience in version control of data and artefacts (like DeltaLake, DVC, or MLFlow), designing and operating cloud-based systems, preferably within AWS or Databricks, familiarity with messaging platforms and proficiency in infrastructure as code (e.g. Terraform, CloudFormation, Pulumi), a grasp of K8s and More ❯

interactive AI demos and proofs-of-concept with Streamlit, Gradio, or Next.js for stakeholder feedback; MLOps & Deployment: Implement CI/CD pipelines (e.g., GitLab Actions, Apache Airflow), experiment tracking (MLflow), and model monitoring for reliable production workflows; Cross-Functional Collaboration: Participate in code reviews, architectural discussions, and sprint planning to deliver features end-to-end. Requirements: Master's degree in More ❯

past, ideally centred around a software product, and have solid Python coding skills, and expertise with cloud infrastructure (preferably AWS). Familiarity with Containers and MLE tools such as MLflow and Airflow is essential, with any knowledge of AI SaaS or GenAI APIs being is a bonus. But what truly matters is your passion for learning and advancing technology. In More ❯