1 to 25 of 53 MLOps Jobs in the City of London

scalable systems and high-volume, low latency, transaction/computation applications. Broad understanding of the software development lifecycle, including the application of LLM driven and Data Science DevOps/MLOps/AIOps methodologies. Expertise in data management and analytics, including data structures, data processing, data modelling, and statistical analysis. Knowledge of Python and C# for development and scripting. Leadership: Proven More ❯

scalable systems and high-volume, low latency, transaction/computation applications. Broad understanding of the software development lifecycle, including the application of LLM driven and Data Science DevOps/MLOps/AIOps methodologies. Expertise in data management and analytics, including data structures, data processing, data modelling, and statistical analysis. Knowledge of Python and C# for development and scripting. Leadership: Proven More ❯

GCP), Kubernetes, and containerised systems. Strong technical skills in Python, Java/Go, TensorFlow, PyTorch, and data engineering. Proven ability to engage directly with CxO-level stakeholders. Experience in MLOps, AI governance, and large-scale deployment. Recognised professional certifications in AI or cloud technologies. If you have these skills and would like to find out more, please apply now. More ❯

GCP), Kubernetes, and containerised systems. Strong technical skills in Python, Java/Go, TensorFlow, PyTorch, and data engineering. Proven ability to engage directly with CxO-level stakeholders. Experience in MLOps, AI governance, and large-scale deployment. Recognised professional certifications in AI or cloud technologies. If you have these skills and would like to find out more, please apply now. More ❯

TensorFlow). Proficiency in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong More ❯

TensorFlow). Proficiency in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong More ❯

research data ✔️ Expertise in Python, R, TensorFlow, PyTorch, and modern ML frameworks ✔️ Hands-on experience with LLMs, generative AI, and natural language processing for medical text ✔️ Familiarity with AWS, MLOps, and cloud-based platforms ✔️ Proven ability to partner with stakeholders and adapt AI solutions to complex healthcare challenges ✔️ Experience in digital healthcare tool development (advantageous) This is a unique opportunity More ❯

AWS, GCP, or Azure) and managing cloud-based data infrastructure. Strong background in database technologies (SQL Server, Redshift, PostgreSQL, Oracle). Desirable Skills: Familiarity with machine learning pipelines and MLOps practices. Additional experience with Databricks and specific AWS such as Glue, S3, Lambda Proficient in Git, CI/CD pipelines, and DevOps tools (e.g., Azure DevOps) Hands-on experience with More ❯

AWS, GCP, or Azure) and managing cloud-based data infrastructure. Strong background in database technologies (SQL Server, Redshift, PostgreSQL, Oracle). Desirable Skills: Familiarity with machine learning pipelines and MLOps practices. Additional experience with Databricks and specific AWS such as Glue, S3, Lambda Proficient in Git, CI/CD pipelines, and DevOps tools (e.g., Azure DevOps) Hands-on experience with More ❯

investigate failure cases or drift, and recommend iterative improvements Track and report key performance and operational metrics Contribute to best practices in version control, testing, CI/CD, and MLOps Share expertise through peer code reviews, documentation, and collaborative learning initiatives Stay current with emerging trends in AI/ML, particularly in areas such as LLMs, NLP, and intelligent document More ❯

in NLP, unstructured data, and information extraction Eagerness to learn, take feedback, and contribute to a collaborative team Nice to Have: Experience with SQL/NoSQL databases Familiarity with MLOps tools (Docker, Git, CI/CD) Exposure to vector databases or semantic search Knowledge of financial datasets or document processing workflows If you’re excited to grow your skills in More ❯

in NLP, unstructured data, and information extraction Eagerness to learn, take feedback, and contribute to a collaborative team Nice to Have: Experience with SQL/NoSQL databases Familiarity with MLOps tools (Docker, Git, CI/CD) Exposure to vector databases or semantic search Knowledge of financial datasets or document processing workflows If you’re excited to grow your skills in More ❯

frameworks (such as Scikit-learn, TensorFlow, PyTorch, Hugging Face) and data manipulation tools (Pandas, NumPy), as well as version control, containerisation, and ML deployment pipelines. Understands how to apply MLOps principles in production environments To be successful in this role you must have hands-on experience designing and deploying ML models in industryideally within agile or cross-functional teams. Understands … to make a difference. Required Knowledge and experience Technical skills: Confident user of MSTeams Strong applied experience in machine learning and/or data science roles Solid understanding of MLOps and production deployment practices Experience with Python and core ML libraries (e.g., Scikit-learn, Pandas, PyTorch, TensorFlow) Familiarity with cloud platforms and data infrastructure (e.g., AWS/GCP/Azure More ❯

client-facing 🎯 Must-haves: 8+ years in Data Science/ML Engineering 2+ years hands-on with Databricks Strong track record delivering production-grade ML models Solid grasp of MLOps best practices Confident speaking to technical and non-technical stakeholders 🛠️ Tech you’ll be using: Python, SQL, Spark, R MLflow, vector databases GitHub/GitLab/Azure DevOps Jira, Confluence More ❯

a talented team of researchers to improve and refine algorithms for real-world tasks Ensure the scalability and maintainability of production systems, with a strong focus on (low-level) MLOps What You’ll Be Doing (Machine Learning Researcher): Conduct groundbreaking research to develop new algorithms and models for robotics Work on advanced learning approaches such as reinforcement learning and imitation More ❯

a talented team of researchers to improve and refine algorithms for real-world tasks Ensure the scalability and maintainability of production systems, with a strong focus on (low-level) MLOps What You’ll Be Doing (Machine Learning Researcher): Conduct groundbreaking research to develop new algorithms and models for robotics Work on advanced learning approaches such as reinforcement learning and imitation More ❯

Strong communication and stakeholder management skills, with the ability to influence at C-level Background as a hands-on data scientist, with deep understanding of model development, deployment, and MLOps MSc in a quantitative field required; PhD welcome but not essential Familiarity with AI governance and ethical AI practices Curious and up to date on GenAI technologies (either directly or More ❯

deployment of high-impact models (personalisation, churn, demand forecasting, pricing, etc.) into production. Establish governance and standards for model quality, explainability, and bias mitigation across jurisdictions. Drive adoption of MLOps best practices - CI/CD, automated testing, monitoring, feature stores. Partner with Marketing, Digital, Technology, Innovation and local business units to embed intelligence into customer and operational workflows. Champion advanced More ❯

deployment of high-impact models (personalisation, churn, demand forecasting, pricing, etc.) into production. Establish governance and standards for model quality, explainability, and bias mitigation across jurisdictions. Drive adoption of MLOps best practices - CI/CD, automated testing, monitoring, feature stores. Partner with Marketing, Digital, Technology, Innovation and local business units to embed intelligence into customer and operational workflows. Champion advanced More ❯

rest/in-transit), token management Identity integration (Google Identity, Azure Entra ID) Data Security & Governance RAG pipelines with data classification, masking, and DLP GDPR and data residency compliance MLOps & DevSecOps GitHub Actions, CI/CD security testing, model drift detection, audit logging Lab Environment Tooling Infrastructure-as-Code (IaC): Terraform, Pulumi Containerization & Orchestration: Docker, Kubernetes (GKE/AKS) Networking More ❯

direct experience working with YOLO models being a major plus. Experience with large-scale data processing and database technologies such as MongoDB or other NoSQL databases. Practical knowledge of MLOps principles, including CI/CD pipelines using GitHub Actions . Experience with analytics platforms like Google Analytics and business intelligence tools like Tableau or Power BI. Knowledge of GPU-accelerated More ❯

direct experience working with YOLO models being a major plus. Experience with large-scale data processing and database technologies such as MongoDB or other NoSQL databases. Practical knowledge of MLOps principles, including CI/CD pipelines using GitHub Actions . Experience with analytics platforms like Google Analytics and business intelligence tools like Tableau or Power BI. Knowledge of GPU-accelerated More ❯

their Databricks practice and needs multiple specialists to join this high-impact initiative. 🎯 DATABRICKS TEAM ROLES: Senior Data Engineering Consultant - Databricks/Snowflake focus Senior ML Engineer - Databricks specialisation (MLOps) Senior Platform Engineer - Databricks deployment expert Senior Databricks ML Engineer - Premium specialist role 🏢 Join a Dedicated Databricks Centre of Excellence: You'll be part of a specialist Databricks team delivering More ❯

their Databricks practice and needs multiple specialists to join this high-impact initiative. 🎯 DATABRICKS TEAM ROLES: Senior Data Engineering Consultant - Databricks/Snowflake focus Senior ML Engineer - Databricks specialisation (MLOps) Senior Platform Engineer - Databricks deployment expert Senior Databricks ML Engineer - Premium specialist role 🏢 Join a Dedicated Databricks Centre of Excellence: You'll be part of a specialist Databricks team delivering More ❯

engineering, optimisation). Excellent communication and stakeholder management abilities. A track record of managing teams and shaping development roadmaps. experience with cloud environments (AWS, Azure, GCP), and familiarity with MLOps lifecycle or solution architecture. More ❯