1 to 25 of 181 Permanent MLOps Jobs in England

scalable AI applications across the enterprise. Work with a multi-functional team (DevOps, data engineers, developers, testers, Infosec) to productionize AI services on AWS. Enhance our tech stack, including MLOps, CI/CD pipelines, UI, and AI Python libraries. Develop prompts, fine-tune models, and track results. Evaluate third-party GenAI and LLM technologies for cost-benefit analysis. Deliver projects More ❯

scaling tech company with clear progression opportunities. Collaborative culture: A dynamic team that values innovation, experimentation, and learning. Cutting-edge tools: Access to modern data platforms, cloud infrastructure, and MLOps frameworks. Competitive package: Salary, bonus, and flexible benefits. If you’re a driven Data Scientist ready to shape the future of technology and software innovation, we’d love to hear More ❯

and optimize LLMs for quality, latency, sustainability and cost-effective performance. Programming and NLP Tooling: Advanced Python proficiency and expertise with frameworks like PyTorch, TensorFlow, Hugging Face, or LangChain. MLOps and Deployment: Experience with containerization tools (Docker, Kubernetes) and workflow management tools (Azure ML Studio, MLFlow). Cloud and AI Infrastructure: Hands-on experience with (preferably Azure) Cloud environments for More ❯

compliant deployment of AI and Generative AI systems. Broad experience across major cloud platforms (AWS, Azure, GCP) with Generative AI services. Cloud-agnostic experience is preferred. Strong grasp of MLOps/LLMOps principles, including CI/CD for ML, model monitoring, and governance frameworks. Proficiency with large-scale data processing and storage technologies (SQL, Spark, Hadoop) is a plus. Excellent More ❯

programming, software design, i.e., SOLID principles, and testing practices. Knowledge and working experience of AGILE methodologies. Proficient with SQL. Familiarity with Databricks, Spark, geospatial data/modelling Exposure to MLOps, model monitoring principles, CI/CD and associated tech, e.g., Docker, MLflow, k8s, FastAPI etc. are a plus. Additional Information What’s in it for you?: Competitive salary that reflects More ❯

Collaborate closely with Product Managers and GenAI/Data Science leaders to translate business vision into technical delivery. Scalability & Reliability Implement frameworks for CI/CD, testing, observability, and MLOps integration. Ensure systems meet performance, uptime, and data security standards required by global CPG enterprises. Plan for multi-market, multi-category deployments. Long-term Productization Create a 5-year technical More ❯

TensorFlow, Keras). Solid understanding of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow). Strong problem-solving skills and ability to work independently in a fast-paced environment. Preferred Qualifications: MSc or PhD in Computer Science More ❯

TensorFlow, Keras). Solid understanding of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow). Strong problem-solving skills and ability to work independently in a fast-paced environment. Preferred Qualifications: MSc or PhD in Computer Science More ❯

TensorFlow, Keras). Solid understanding of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow). Strong problem-solving skills and ability to work independently in a fast-paced environment. Preferred Qualifications: MSc or PhD in Computer Science More ❯

TensorFlow, Keras). Solid understanding of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow). Strong problem-solving skills and ability to work independently in a fast-paced environment. Preferred Qualifications: MSc or PhD in Computer Science More ❯

TensorFlow, Keras). Solid understanding of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow). Strong problem-solving skills and ability to work independently in a fast-paced environment. Preferred Qualifications: MSc or PhD in Computer Science More ❯

Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: 💰 Competitive Salary & Bonus : £40,000 – £45,000 plus bonus 🏡 Hybrid Working : A flexible mix of office and remote work 📈 Career Growth : Structured More ❯

Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: 💰 Competitive Salary & Bonus : £40,000 – £45,000 plus bonus 🏡 Hybrid Working : A flexible mix of office and remote work 📈 Career Growth : Structured More ❯

Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: 💰 Competitive Salary & Bonus : £40,000 – £45,000 plus bonus 🏡 Hybrid Working : A flexible mix of office and remote work 📈 Career Growth : Structured More ❯

Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: 💰 Competitive Salary & Bonus : £40,000 – £45,000 plus bonus 🏡 Hybrid Working : A flexible mix of office and remote work 📈 Career Growth : Structured More ❯

Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: 💰 Competitive Salary & Bonus : £40,000 – £45,000 plus bonus 🏡 Hybrid Working : A flexible mix of office and remote work 📈 Career Growth : Structured More ❯

real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from SQL to large-scale distributed data tools (Spark, etc.). Strong written and verbal communication skills, especially More ❯

building more effective autonomous agents for complex property management workflows and decision-making processes Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch), LLM orchestration tools (LangChain, LangGraph), MLOps practices and tooling (such as MLflow, Kubeflow, or similar), vector databases, and cloud platforms (AWS, Azure, GCP) with their AI/ML offerings Preferably hands-on experience with voice technologies More ❯

model endpoints, pipelines, embeddings, vector search) or equivalent cloud-native ML platforms (e.g. AWS SageMaker, Azure ML) and agent orchestration frameworks such as LangChain and LangGraph Solid understanding of MLOps - CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying and operating ML systems in production (batch and real-time). Familiarity with RAG architectures, prompt More ❯

have: Building SDKs or client libraries to support API consumption. Knowledge of distributed data processing frameworks (Spark, Dask). Understanding of GPU orchestration and optimization in Kubernetes. Familiarity with MLOps and ML model lifecycle pipelines. Experience with AI model training and fine-tuning. Familiarity with event-driven architecture and messaging frameworks like Kafka. Experience with NoSQL datastores like Cassandra Our More ❯

class developer experience to accelerate customer success. Deployment & Integration: Master and advise on modern cloud deployment patterns using containers (e.g., Docker) and cloud images. Help customers integrate with their MLOps pipelines and data workflows. Voice of the Customer: Act as the primary technical point of contact for our users. Systematically gather customer feedback, identify pain points, and translate them into More ❯

Strong experience in Python, TensorFlow, PyTorch, Scikit-learn or similar frameworks. Solid understanding of machine learning, deep learning, NLP, or computer vision. Hands-on with data engineering, model deployment (MLOps), and cloud platforms (AWS, Azure, GCP). Strong problem-solving, algorithmic, and analytical skills. Knowledge of big data tools (Spark, Hadoop) is a plus. More ❯

Strong experience in Python, TensorFlow, PyTorch, Scikit-learn or similar frameworks. Solid understanding of machine learning, deep learning, NLP, or computer vision. Hands-on with data engineering, model deployment (MLOps), and cloud platforms (AWS, Azure, GCP). Strong problem-solving, algorithmic, and analytical skills. Knowledge of big data tools (Spark, Hadoop) is a plus. 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 ❯