151 to 175 of 273 MLOps Jobs in London

for ML developments within a scaleup environment. What you’ll do: Architect and build scalable ML systems in GCP and AWS Drive end-to-end delivery across experimentation, modelling, MLOps and deployment Set technical direction, mentor teams, and champion best practices Work closely with product and data teams to turn ideas into real-world impact Collaborate with 3rd party vendors More ❯

stories, change requests, and screen designs. A background as a Business Analyst is preferred. Experience in workforce management, scheduling, HR tech, optimisation domains, AI/ML productisation, LLM integration, MLOps, or enterprise integration standards (ETL, REST APIs, webhooks, event streaming) is a bonus. At the centre of the company’s culture is freedom and openness which takes a lot of More ❯

stories, change requests, and screen designs. A background as a Business Analyst is preferred. Experience in workforce management, scheduling, HR tech, optimisation domains, AI/ML productisation, LLM integration, MLOps, or enterprise integration standards (ETL, REST APIs, webhooks, event streaming) is a bonus. At the centre of the company’s culture is freedom and openness which takes a lot of More ❯

stories, change requests, and screen designs. A background as a Business Analyst is preferred. Experience in workforce management, scheduling, HR tech, optimisation domains, AI/ML productisation, LLM integration, MLOps, or enterprise integration standards (ETL, REST APIs, webhooks, event streaming) is a bonus. At the centre of the company's culture is freedom and openness which takes a lot of More ❯

Familiarity with REST APIs , websockets, and authentication/security best practices. A disciplined engineering mindset — you think about reliability, maintainability, and long-term scalability. Nice-to-haves Experience with MLOps and model-serving infrastructure. Background in high-performance computing or large-scale scientific workloads. Expertise in data pipeline architecture and real-time streaming systems. Knowledge of scientific or biological computing More ❯

Familiarity with REST APIs , websockets, and authentication/security best practices. A disciplined engineering mindset — you think about reliability, maintainability, and long-term scalability. Nice-to-haves Experience with MLOps and model-serving infrastructure. Background in high-performance computing or large-scale scientific workloads. Expertise in data pipeline architecture and real-time streaming systems. Knowledge of scientific or biological computing More ❯

but Valuable) Worked at a Pre-Seed or Seed-stage startup through to Series A Built or deployed software used by enterprise customers Experience with applied machine learning or MLOps (not just academic or notebook-based work) Exposure to event-driven architectures, streaming systems (Kafka, etc.), or real-time data pipelines Frontend experience in React, TypeScript, or similar modern frameworks More ❯

but Valuable) Worked at a Pre-Seed or Seed-stage startup through to Series A Built or deployed software used by enterprise customers Experience with applied machine learning or MLOps (not just academic or notebook-based work) Exposure to event-driven architectures, streaming systems (Kafka, etc.), or real-time data pipelines Frontend experience in React, TypeScript, or similar modern frameworks More ❯

but Valuable) Worked at a Pre-Seed or Seed-stage startup through to Series A Built or deployed software used by enterprise customers Experience with applied machine learning or MLOps (not just academic or notebook-based work) Exposure to event-driven architectures, streaming systems (Kafka, etc.), or real-time data pipelines Frontend experience in React, TypeScript, or similar modern frameworks More ❯

but Valuable) Worked at a Pre-Seed or Seed-stage startup through to Series A Built or deployed software used by enterprise customers Experience with applied machine learning or MLOps (not just academic or notebook-based work) Exposure to event-driven architectures, streaming systems (Kafka, etc.), or real-time data pipelines Frontend experience in React, TypeScript, or similar modern frameworks More ❯

security and compliance requirements in financial services (FCA, DORA, PRA) Familiarity with CI/CD pipelines (GitHub Actions, Jenkins, or CodePipeline) Desirable exposure to SageMaker , model lifecycle integration, and MLOps monitoring Role Overview You’ll be part of a growing platform engineering function building the next generation of secure AWS services. This position suits a technically strong architect who codes More ❯

security and compliance requirements in financial services (FCA, DORA, PRA) Familiarity with CI/CD pipelines (GitHub Actions, Jenkins, or CodePipeline) Desirable exposure to SageMaker , model lifecycle integration, and MLOps monitoring Role Overview You’ll be part of a growing platform engineering function building the next generation of secure AWS services. This position suits a technically strong architect who codes More ❯

with internal policies and external regulations. Provide technical input on risk mitigation strategies and onboarding documentation. Security & DevSecOps Integration: Integrate AI security controls into CI/CD pipelines and MLOps workflows. Use tools such as Azure Key Vault, Microsoft Entra ID, and GitHub Actions for secure deployment and access management. Monitor AI systems using Azure Monitor, Log Analytics, and Application More ❯

in data science and machine learning. Developers should be familiar with libraries like NumPy, pandas, scikit-learn, TensorFlow, and PyTorch. Familiarity with best practice in Machine Learning workflows and MLOps tools. Fluency in validation and evaluation framework and metrics frameworks for machine learning such as Accuracy, Recall, F1, Intersection over Union, and others. Additional beneficial experience includes: Experience in the More ❯

in data science and machine learning. Developers should be familiar with libraries like NumPy, pandas, scikit-learn, TensorFlow, and PyTorch. Familiarity with best practice in Machine Learning workflows and MLOps tools. Fluency in validation and evaluation framework and metrics frameworks for machine learning such as Accuracy, Recall, F1, Intersection over Union, and others. Additional beneficial experience includes: Experience in the More ❯

Senior DevOps Engineer (MLOps/LLMOps) Clearance: Eligible for BPSS Start: ASAP Work pattern: Hybrid (London) Work type: 12 month FTC (Competitive Salary) We’re working with a major UK government initiative that’s shaping the future of how citizens interact with public services. The programme focuses on harnessing the latest AI technologies to deliver simpler, faster, and more efficient … Azure, or GCP) using Python-based Infrastructure as Code (Terraform or Pulumi). Build and optimise CI/CD pipelines to automate the deployment of AI applications and models (MLOps/LLMOps). Containerise workloads using Docker and manage deployments through Kubernetes (KubeRay, Kubeflow, MLRun, or similar). Provision and manage the infrastructure required to run AI workloads, including vector … Experience Strong background as a DevOps or Cloud Engineer in public cloud environments (AWS, Azure, or GCP). Experience deploying and managing infrastructure for AI/ML workloads using MLOps or LLMOps practices. Excellent scripting and automation skills in Python (e.g. Boto3, SDKs). Proven experience with Python-based IaC frameworks (Pulumi, Terraform, CDKs). Hands-on experience building CI More ❯

Senior DevOps Engineer (MLOps/LLMOps) Clearance: Eligible for BPSS Start: ASAP Work pattern: Hybrid (London) Work type: 12 month FTC (Competitive Salary) We’re working with a major UK government initiative that’s shaping the future of how citizens interact with public services. The programme focuses on harnessing the latest AI technologies to deliver simpler, faster, and more efficient … Azure, or GCP) using Python-based Infrastructure as Code (Terraform or Pulumi). Build and optimise CI/CD pipelines to automate the deployment of AI applications and models (MLOps/LLMOps). Containerise workloads using Docker and manage deployments through Kubernetes (KubeRay, Kubeflow, MLRun, or similar). Provision and manage the infrastructure required to run AI workloads, including vector … Experience Strong background as a DevOps or Cloud Engineer in public cloud environments (AWS, Azure, or GCP). Experience deploying and managing infrastructure for AI/ML workloads using MLOps or LLMOps practices. Excellent scripting and automation skills in Python (e.g. Boto3, SDKs). Proven experience with Python-based IaC frameworks (Pulumi, Terraform, CDKs). Hands-on experience building CI More ❯

Science Engineer to design, develop, and deliver AI and analytics solutions aligned with the organisations Data & AI strategy. Key Responsibilities End-to-end development of AI/ML solutions. MLOps practices: CI/CD, model monitoring, retraining. Use of open-source and enterprise tools (LangChain, Azure OpenAI, Databricks). Generative AI features: embeddings, RAG, AI agents. Clean, testable code with … years in production-level AI/ML delivery. Legal/professional services experience is a plus. AI/ML frameworks: PyTorch, TensorFlow, LangChain. Cloud: Azure (preferred), AWS, GCP. MLOps: CI/CD, model lifecycle, monitoring. Generative AI: LLMs, RAG, chat agents. Data engineering alignment: ETL, governance. Strong coding, communication, and collaboration skills. Strategic thinking, problem-solving, and stakeholder engagement. More ❯

Science Engineer to design, develop, and deliver AI and analytics solutions aligned with the organisations Data & AI strategy. Key Responsibilities End-to-end development of AI/ML solutions. MLOps practices: CI/CD, model monitoring, retraining. Use of open-source and enterprise tools (LangChain, Azure OpenAI, Databricks). Generative AI features: embeddings, RAG, AI agents. Clean, testable code with … years in production-level AI/ML delivery. Legal/professional services experience is a plus. AI/ML frameworks: PyTorch, TensorFlow, LangChain. Cloud: Azure (preferred), AWS, GCP. MLOps: CI/CD, model lifecycle, monitoring. Generative AI: LLMs, RAG, chat agents. Data engineering alignment: ETL, governance. Strong coding, communication, and collaboration skills. Strategic thinking, problem-solving, and stakeholder engagement. More ❯

Science Engineer to design, develop, and deliver AI and analytics solutions aligned with the organisations Data & AI strategy. Key Responsibilities * End-to-end development of AI/ML solutions. * MLOps practices: CI/CD, model monitoring, retraining. * Use of open-source and enterprise tools (LangChain, Azure OpenAI, Databricks). * Generative AI features: embeddings, RAG, AI agents. * Clean, testable code with … years in production-level AI/ML delivery. * Legal/professional services experience is a plus. * AI/ML frameworks: PyTorch, TensorFlow, LangChain. * Cloud: Azure (preferred), AWS, GCP. * MLOps: CI/CD, model lifecycle, monitoring. * Generative AI: LLMs, RAG, chat agents. * Data engineering alignment: ETL, governance. * Strong coding, communication, and collaboration skills. * Strategic thinking, problem-solving, and stakeholder engagement. In More ❯

and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). … loading (ETL). Experience with SQL databases (SQL Server), data validation, cleansing workflows, scheduling tools (Azure Data Factory), and ensuring data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing CI/ More ❯

and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). … loading (ETL). Experience with SQL databases (SQL Server), data validation, cleansing workflows, scheduling tools (Azure Data Factory), and ensuring data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing CI/ More ❯

and deploy production-grade AI systems tailored to audit applications. Contribute to architectural decisions and solution governance. Write clean, efficient, and scalable code that adheres to software engineering principles, MLOps practices, and cloud-native development. AI Solution Delivery: Own the implementation of ML pipelines, APIs, and data integration workflows. Operational Excellence: Contribute to defining reusable development patterns, enforcing coding standards … and implementing MLOps best practices that support version control, performance optimisation, and maintainability. Cross-Disciplinary Collaboration: Work side-by-side with data scientists, product managers, platform engineers, and QA teams to align on technical requirements, delivery timelines, and integration plans. Provide hands-on contributions to ensure AI capabilities are smoothly embedded within core audit platforms and services. Capability Building & Knowledge More ❯

and deploy production-grade AI systems tailored to audit applications. Contribute to architectural decisions and solution governance. Write clean, efficient, and scalable code that adheres to software engineering principles, MLOps practices, and cloud-native development. AI Solution Delivery: Own the implementation of ML pipelines, APIs, and data integration workflows. Operational Excellence: Contribute to defining reusable development patterns, enforcing coding standards … and implementing MLOps best practices that support version control, performance optimisation, and maintainability. Cross-Disciplinary Collaboration: Work side-by-side with data scientists, product managers, platform engineers, and QA teams to align on technical requirements, delivery timelines, and integration plans. Provide hands-on contributions to ensure AI capabilities are smoothly embedded within core audit platforms and services. Capability Building & Knowledge More ❯

ALTEN , an engineering and technology consultancy, We are a leading Engineering and IT consultancy operating across 30 countries, making waves in all sectors: Aeronautics, Space, Defence, Security and Naval, Automotive, Rail and mobility, Energy and environment, Life Sciences and health More ❯