1 to 25 of 414 MLOps Jobs in the UK

or experimental Jupyter notebooks to full production deployment. Strong programming skills in Python, with familiarity in ML libraries/frameworks such as TensorFlow, PyTorch, and Scikit-learn. Experience with MLOps practices including model drift detection, decay, A/B testing, integration testing, differential testing, Python package building, and code version control. Skilled in data pipeline creation and working with both More ❯

SQL, NoSQL, and big data technologies (Spark, Hadoop, Snowflake, Databricks, etc.). Strong background in statistical modelling, probability theory, and mathematical optimization. Experience deploying machine learning models to production (MLOps, Docker, Kubernetes, etc.). Familiarity with AWS/GCP/Azure cloud ML platforms for scalable model training and inference. Strong problem-solving, communication, and business acumen skills. Experience in More ❯

in an agile environment where experimentation, pragmatic engineering, and rapid iteration are key to creating business value. Leverage modern tools and methods: Use contemporary ML frameworks, cloud platforms, and MLOps best practices to build scalable, reusable solutions. Communicate insights clearly: Distill complex technical findings into concise, actionable narratives for technical and business audiences alike. Keep learning and pushing boundaries: Expand More ❯

in an agile environment where experimentation, pragmatic engineering, and rapid iteration are key to creating business value. Leverage modern tools and methods: Use contemporary ML frameworks, cloud platforms, and MLOps best practices to build scalable, reusable solutions. Communicate insights clearly: Distill complex technical findings into concise, actionable narratives for technical and business audiences alike. Keep learning and pushing boundaries: Expand More ❯

for the members of the team. Foster and grow the data team's skillsets, culture and ways of working. Provide technical oversight of analytical approaches, code and model reviews, MLOps standards, data governance compliance, and help to unblock complex challenges Be able to technically manage projects Lead on proposals and RfP responses Lead global data initiatives and communities of practice More ❯

LangChain, LlamaIndex, and RAG architectures. Build APIs and microservices for scalable AI deployment. Use AI-powered dev tools like GitHub Copilot, Cursor, and Codeium to speed up iteration. Apply MLOps/LLMOps practices with MLflow, Weights & Biases, and Kubeflow. ?? Youll Bring Strong Python skills and experience with LangChain, Transformers, Hugging Face. Solid grasp of LLM behavior, prompt optimization, and data More ❯

LangChain, LlamaIndex, and RAG architectures. Build APIs and microservices for scalable AI deployment. Use AI-powered dev tools like GitHub Copilot, Cursor, and Codeium to speed up iteration. Apply MLOps/LLMOps practices with MLflow, Weights & Biases, and Kubeflow. Youll Bring Strong Python skills and experience with LangChain, Transformers, Hugging Face. Solid grasp of LLM behavior, prompt optimization, and data More ❯

LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor More ❯

familiar with process management tools such as JIRA, Target process, Trello or similar Nice to Have: ● Familiarity with data security, privacy, and compliance frameworks ● Exposure to machine learning pipelines, MLOps, or AI-driven data products ● Experience with big data platforms and technologies such as EMR, Databricks, Kafka, Spark ● Exposure to AI/ML concepts and collaboration with data science or More ❯

familiar with process management tools such as JIRA, Target process, Trello or similar Nice to Have: ● Familiarity with data security, privacy, and compliance frameworks ● Exposure to machine learning pipelines, MLOps, or AI-driven data products ● Experience with big data platforms and technologies such as EMR, Databricks, Kafka, Spark ● Exposure to AI/ML concepts and collaboration with data science or More ❯

platform delivery methodologies (Agile/DevOps) Deep expertise in: Machine learning, deep learning, NLP, computer vision Data engineering, big data platforms, and analytics AI/ML model lifecycle management (MLOps) Cloud-native architectures and scalable AI infrastructure Strong understanding of emerging technologies (e.g., generative AI, edge AI, synthetic data) Strategic & Business Acumen Ability to translate complex AI capabilities into business More ❯

principles, and testing practices. Knowledge and working experience of AGILE methodologies. Proficient with SQL. Familiarity with Databricks, Spark, geospatial data/modelling and insurance are a plus. Exposure to MLOps, model monitoring principles, CI/CD and associated tech, e.g., Docker, MLflow, k8s, FastAPI etc are desirable We’ll help you gain Experience working in a high-performance environment where More ❯

to strengthen our Systematic Research team, we are looking for a London based: Quantitative Analyst We are seeking a hands on quantitative researcher with strong software/DevOps/MLOps capability to develop, productionise, and scale systematic investment models and sustainability analytics. You will own the end to end research to production lifecycle : automating data operations, building and validating quant More ❯

explainability (e.g., SHAP/LIME) and scalable ML systems (including generative AI, NLP, CV, and recommendation engines). - Partner with engineering teams to ensure robust deployment and adherence to MLOps principles. - Shape and consult on broader data strategy and infrastructure. - Mentor and coach junior team members while staying ahead of emerging trends in AI and machine learning. Requirements - Degree in More ❯

explainability (e.g., SHAP/LIME) and scalable ML systems (including generative AI, NLP, CV, and recommendation engines). - Partner with engineering teams to ensure robust deployment and adherence to MLOps principles. - Shape and consult on broader data strategy and infrastructure. - Mentor and coach junior team members while staying ahead of emerging trends in AI and machine learning. Requirements - Degree in More ❯

and ability to communicate complex ideas clearly. Desirable Skills Experience with deep learning architectures (CNNs, RNNs, Transformers). Familiarity with cloud platforms (AWS, GCP, or Azure). Exposure to MLOps tools or pipeline automation. Previous internship or research experience in a related field. What’s on Offer Competitive graduate salary and benefits package. A collaborative, learning-focused environment. Opportunity to More ❯

LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor More ❯

LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor More ❯

LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor More ❯

code. Strong problem-solving skills and the ability to work in fast-paced environments. Excellent communication and stakeholder management skills. Preferred Qualifications: Experience with machine learning data pipelines and MLOps practices. Knowledge of data streaming technologies such as Kafka or Kinesis. Familiarity with Terraform or similar infrastructure automation tools. Previous experience working in consulting or client-facing roles. What We More ❯

code. Strong problem-solving skills and the ability to work in fast-paced environments. Excellent communication and stakeholder management skills. Preferred Qualifications: Experience with machine learning data pipelines and MLOps practices. Knowledge of data streaming technologies such as Kafka or Kinesis. Familiarity with Terraform or similar infrastructure automation tools. Previous experience working in consulting or client-facing roles. What We 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 ❯

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 ❯

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 ❯

for predominantly time-series forecasting Collaborate with data scientists and researchers to productionise models Manage cloud-based and on-prem setup Requirements: 25 years experience in ML engineering/MLOps roles Strong proficiency in Python and experience with relevant ML libraries/frameworks (pandas, numpy, sklearn, TensorFlow, PyTorch) Strong understanding and experience in implementing end-to-end ML pipelines (data More ❯