151 to 175 of 319 MLflow Jobs in England

like BERT or GPT. Advanced proficiency in Python, including PyTorch, Hugging Face Transformers, Pandas, and scikit-learn. Strong understanding of cloud services (preferably AWS) and experience with Docker, Kubernetes, MLFlow, Kubeflow, or similar MLOps tools. 📩 Interested? Apply below or email me at mmatysik@trg-uk.com. More ❯

hands-on application in a risk, compliance or security-focused role. Strong proficiency in Python and statistical analysis. Familiarity with LLMs, ML pipeline management and AI lifecycle tools (e.g., MLflow, ModelOps platforms). Excellent communication and documentation skills for technical and non-technical stakeholders. Bachelor’s or Master’s degree in Machine Learning, AI, Computer Science, Statistics, Mathematics or a More ❯

training data pipelines, including data gathering, cleaning, augmentation, labeling, and managing vector databases for large-scale RAG workflows. Possess skills in model deployment, monitoring, versioning, and continuous improvement frameworks (MLflow, AWS SageMaker Model Monitor), ensuring models meet scalability, latency, and operational performance requirements. Have experience with deep learning frameworks (TensorFlow, PyTorch), AWS SageMaker, Bedrock, Lambda, and familiarity with Azure AI More ❯

in DevOps, cloud infrastructure, or site reliability engineering. Strong expertise in multi-cloud and hybrid infrastructure including AWS, GCP, and on-premises environments. Experience with MLOps tooling such as MLFlow, Kubeflow, DataRobot, or similar platforms for ML lifecycle management. Experience with containerization and orchestration (Docker, Kubernetes) specifically for ML workloads and GPU clusters. Deep understanding of CI/CD pipelines More ❯

and model performance. Cloud and MLOps for Optimization Models: Familiarity with deploying and managing optimization models on cloud platforms (AWS, GCP, Azure) and employing MLOps practices (with tools like MLFlow, BentoML) to ensure efficient lifecycle management of optimization solutions. Ethical AI and Continuous Learning: A robust understanding of AI ethics and privacy considerations, especially relevant to optimization, coupled with a More ❯

and ETL processes Good knowledge of ML ops principles and best practices to deploy, monitor and maintain machine learning models in production Familiarity with Git CI/CD and MLflow for managing and tracking code deployment or model versions Experience with cloud-based data platforms such as AWS or Google Cloud Platform Nice to have: Experience with Kafka Proven track More ❯

What You'll Do - Design and build an end-to-end MLOps pipeline using AWS , with a strong focus on SageMaker for training, deployment, and hosting. - Integrate and operationalize MLflow for model versioning, experiment tracking, and reproducibility. - Architect and implement a feature store strategy for consistent, discoverable, and reusable features across training and inference environments (e.g., using SageMaker Feature Store … years of experience in MLOps, DevOps, or ML infrastructure roles. - Deep familiarity with AWS services , especially SageMaker , S3, Lambda, CloudWatch, IAM, and optionally Glue or Athena. - Strong experience with MLflow , experiment tracking , and model versioning. - Proven experience setting up and managing a feature store , and driving best practices for feature engineering in production systems . - Proficiency in model testing strategies More ❯

techniques. Have experience with Cloud infrastructure (ideally AWS), DevOps technologies such as Docker or Terraform and CI/CD processes and tools. Have previously worked with MLOps tools like MLFlow and Airflow, or on common problems such as model and API monitoring, data drift and validation, autoscaling, access permissions Have previously worked with monitoring tools such as New Relic or … associated ML/DS libraries (scikit-learn, numpy, pandas, LightGBM, LangChain/LangGraph, TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, ECR, Athena, etc. MLOps: Terraform, Docker, Spacelift, Airflow, MLFlow Monitoring: New Relic CI/CD: Jenkins, Github Actions More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase More ❯

containerization, and cloud deployment for large-scale models. Solid programming skills in Python and familiarity with machine learning frameworks like TensorFlow, PyTorch, Hugging Face Transformers, and MLOps tools (e.g., MLflow, Kubeflow). Strong analytical and problem-solving skills, with an aptitude for translating complex theoretical research into practical applications. Day to Day Conduct research and implementation on the development, training More ❯

to non-technical stakeholders. Expertise in identifying and mitigating bias in AI/ML models. Proficiency in Python and familiarity with tools/platforms like Azure, AWS, GCP, Databricks, MLFlow, Airflow, Plotly Dash, and Streamlit. Our Benefits Include Group pension plan, life assurance, income protection, and critical illness cover. Private medical and dental insurance. Cyclescheme, Techscheme, and season ticket loans. More ❯

data modeling, data warehousing, data integration, and data governance. Databricks Expertise: They have hands-on experience with the Databricks platform, including its various components such as Spark, Delta Lake, MLflow, and Databricks SQL. They are proficient in using Databricks for various data engineering and data science tasks. Cloud Platform Proficiency: They are familiar with cloud platforms like AWS, Azure, or More ❯

ingestion patterns, storage layers (Delta Lake), processing frameworks (Spark), and consumption mechanisms. Technology Selection: Evaluate and recommend optimal Databricks features and integrations (e.g., Unity Catalog, Photon, Delta Live Tables, MLflow) and complementary cloud services (e.g., Azure Data Factory, Azure Data Lake Storage, Power BI). Security & Governance Frameworks: Design robust data governance, security, and access control models within the Databricks More ❯

ingestion patterns, storage layers (Delta Lake), processing frameworks (Spark), and consumption mechanisms. Technology Selection: Evaluate and recommend optimal Databricks features and integrations (e.g., Unity Catalog, Photon, Delta Live Tables, MLflow) and complementary cloud services (e.g., Azure Data Factory, Azure Data Lake Storage, Power BI). Security & Governance Frameworks: Design robust data governance, security, and access control models within the Databricks More ❯

ingestion patterns, storage layers (Delta Lake), processing frameworks (Spark), and consumption mechanisms. Technology Selection: Evaluate and recommend optimal Databricks features and integrations (e.g., Unity Catalog, Photon, Delta Live Tables, MLflow) and complementary cloud services (e.g., Azure Data Factory, Azure Data Lake Storage, Power BI). Security & Governance Frameworks: Design robust data governance, security, and access control models within the Databricks More ❯

work in‐office in Euston 2–3 days per week (right to work in the UK required). Nice‐to‐haves Clinical or health‐tech domain knowledge. MLOps tooling (MLflow, Kubeflow, Vertex Pipelines). Benefits Competitive salary and attractive equity in a high growth startup 25 days holiday + UK bank holidays. Flexible hours & focus on sustainable pace. Bright office More ❯

work in‐office in Euston 2–3 days per week (right to work in the UK required). Nice‐to‐haves Clinical or health‐tech domain knowledge. MLOps tooling (MLflow, Kubeflow, Vertex Pipelines). Benefits Competitive salary and attractive equity in a high growth startup 25 days holiday + UK bank holidays. Flexible hours & focus on sustainable pace. Bright office More ❯

code (Docker, Terraform, etc) Build efficient data pipelines to handle text and audio data processing for ML models Deploy NLP models in cloud environments (AWS SageMaker) through Jenkins Use MLflow and other ML Ops applications to streamline ML workflows and adhere to data privacy and residency guidelines Communicate your work throughout the team and related departments Collaboration is a key More ❯

tools. Exposure to real-time/streaming data (Kafka, Spark Streaming, etc.). Understanding of data mesh , data contracts , or domain-driven data architecture . Hands on experience with MLflow and Llama #J-18808-Ljbffr More ❯

and deploying LLM-powered applications in production. Familiarity with transformer architectures and practical handling of LLM context, prompt chaining, and prompt engineering. Hands-on experience with MLOps tools like MLflow, Weights & Biases, and orchestration platforms like Kubeflow or Databricks. Deep understanding of cloud platforms (especially Azure and AWS), containers, microservices, and event-driven architecture. Strong problem-solving and debugging skills More ❯

ingestion patterns, storage layers (Delta Lake), processing frameworks (Spark), and consumption mechanisms. Technology Selection: Evaluate and recommend optimal Databricks features and integrations (e.g., Unity Catalog, Photon, Delta Live Tables, MLflow) and complementary cloud services (e.g., Azure Data Factory, Azure Data Lake Storage, Power BI). Security & Governance Frameworks: Design robust data governance, security, and access control models within the Databricks More ❯

code (Docker, Terraform, etc) Build efficient data pipelines to handle text and audio data processing for ML models Deploy NLP models in cloud environments (AWS SageMaker) through Jenkins Use MLflow and other ML Ops applications to streamline ML workflows and adhere to data privacy and residency guidelines Communicate your work throughout the team and related departments Collaboration is a key More ❯

Refinitiv, prime brokerage APIs, and regulatory systems Advanced Security: Zero-trust architecture, FCA compliance, and financial-grade security protocols 🔧 Technical Stack You'll Lead AI/ML: TensorFlow, PyTorch, MLflow, NVIDIA Triton, custom financial LLMs Data: Kafka, Flink, Snowflake, ClickHouse, Neo4j, vector databases Backend: FastAPI, GraphQL, microservices, Docker, Kubernetes Frontend: React/Next.js, TypeScript, real-time dashboards, mobile apps Cloud More ❯

Refinitiv, prime brokerage APIs, and regulatory systems Advanced Security: Zero-trust architecture, FCA compliance, and financial-grade security protocols 🔧 Technical Stack You'll Lead AI/ML: TensorFlow, PyTorch, MLflow, NVIDIA Triton, custom financial LLMs Data: Kafka, Flink, Snowflake, ClickHouse, Neo4j, vector databases Backend: FastAPI, GraphQL, microservices, Docker, Kubernetes Frontend: React/Next.js, TypeScript, real-time dashboards, mobile apps Cloud More ❯

Refinitiv, prime brokerage APIs, and regulatory systems Advanced Security: Zero-trust architecture, FCA compliance, and financial-grade security protocols Technical Stack You'll Lead AI/ML: TensorFlow, PyTorch, MLflow, NVIDIA Triton, custom financial LLMs Data: Kafka, Flink, Snowflake, ClickHouse, Neo4j, vector databases Backend: FastAPI, GraphQL, microservices, Docker, Kubernetes Frontend: React/Next.js, TypeScript, real-time dashboards, mobile apps Cloud More ❯

globally distributed group tackling novel R & D problems that directly impact customers. OUR TECHNOLOGY STACK: Python • Agentic tools (Autogen, Semantic Kernel, LangChain) • SQL • MongoDB • Third-party LLM APIs • LiteLLM • MLflow • Google Cloud Platform • Docker • PyTorch • Hugging Face Transformers • SPARQL • Kubernetes WHAT YOU WILL BE DOING AT BEAMERY Join us at the forefront of transforming how organisations manage talent and plan More ❯