226 to 250 of 414 MLOps Jobs in the UK

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 ❯

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 ❯

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 ❯

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 ❯

s ML research community and technical direction About You Expert in Python and experienced with LLMs , Agentic AI , or code-generation systems Strong engineering fundamentals (software design, testing, DevOps, MLOps) Curious and inventive — comfortable moving between research and implementation Collaborative communicator who thrives in a small, fast-moving team Bonus: experience with workflow orchestration (e.g., Temporal ), browser automation ( Playwright ), CI 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 ❯

and vector search architectures Ability to build autonomous agents that interact with APIs, fetch public data and trigger external actions Experience deploying AI solutions on Azure Strong understanding of MLOps, data engineering and model life cycle management Experience embedding AI features into a SaaS or operational technology platform Experience working in the manufacturing industry or with manufacturing, supply chain or 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 ❯

reservoir engineers, operational technologists) to understand complex business problems and translate them into actionable ML solutions. Build and maintain the necessary infrastructure for model training, versioning, deployment, and monitoring (MLOps). Conduct rigorous data exploration, cleaning, and feature engineering on large, complex, and often sparse energy-related datasets. Evaluate and optimize model performance, ensuring high accuracy, reliability, and interpretability in … a high-stakes operational environment. Stay current with the latest advancements in machine learning, deep learning, and MLOps to continuously improve AI capabilities. Ensure compliance with data privacy, security, and operational safety standards. Essential Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field. Minimum 3+ years of professional experience as an … specifically within the energy, oil & gas, utilities, or a heavy industrial sector where data science was applied to core operational or strategic challenges. Proficiency in designing, implementing, and maintaining MLOps processes in a cloud environment (e.g., Azure, AWS, GCP). Technical Skills: Expertise in Python and its ML ecosystem (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy). Strong background in More ❯