1 to 25 of 84 MLOps Jobs in London

Strong knowledge of Machine Learning algorithms and statistics. Experience with SQL/NoSQL databases and big data tools (Spark, Hadoop preferred). Experience with MLOps tools such as Docker, Kubernetes, CI/CD pipelines. Preferred Skills:- Experience with LLMs/Generative AI (OpenAI, Hugging Face, LangChain). Cloud experience ...

serving, versioning, and lifecycle management Lead documentation, knowledge transfer, and mentoring to upskill internal teams Technical requirements Strong Python and ML frameworks (PyTorch, TensorFlow) MLOps tooling (MLflow, Kubeflow or similar) Databricks Docker, Kubernetes, CI/CD pipelines Cloud platforms (AWS, Azure or GCP); Terraform experience preferred Financial services experience highly ...

Take validated prototypes from our Data Science team and re-engineer them into high-performance, maintainable codebases that integrate seamlessly with client ecosystems. Infrastructure & MLOps: Build and maintain CI/CD pipelines specifically for machine learning, ensuring that deployments are predictable, reproducible, and secure. Performance Optimization: Profile and optimize model ...

integrations that embed AI into customer journeys, internal tools, and digital platforms* Own model lifecycle processes including deployment, monitoring, evaluation, and retraining* Implement robust MLOps practices to ensure reliability, performance, and scalability* Mentor engineers and contribute to shared tooling, reusable patterns, and engineering standards Required Experience* Strong software engineering background ...

every model we build is fair, free from bias, and explainable to consumers, customers, and regulators. Engineering & Deployment: Work within the engineering team on MLOps to containerise, deploy, and monitor models in high-scale production. Skills & Requirements Core Data Science: Foundational Stats: You must have an excellent grasp of Linear ...

Design and implement Onsera's agentic AI platform – architect LLM/agent framework selection, standardized patterns for tools, memory, guardrails, evaluation, and observability Define MLOps protocols for agentic systems – environment separation, versioning of prompts, tools, and policies, cost controls, rate-limiting, and fail-safe mechanisms Bridge data science and platform ...

TensorFlow, PyTorch, or scikit-learn Eagerness to learn, curiosity, and a problem-solving mindset Nice to Have Cloud experience (AWS, GCP, Azure) Familiarity with MLOps tools (Docker, MLflow, Kubeflow) Deep learning knowledge (CNNs, RNNs, Transformers) Internship, research, or academic projects in ML/AI Why Join Hands-on experience with ...

Mathematics Computer Science or a related quantitative discipline Desirable Skills for the Data Scientist: Experience with deep learning frameworks advanced modelling techniques Familiarity with MLOps concepts model monitoring deployment pipelines Exposure to tools such as Databricks MLflow feature stores Experience working in Agile product-led delivery environments Understanding of data ...

Microsoft AI Foundry or Ollama advantageous). Strong grasp of CI/CD, testing, version control, secure coding, and modern engineering workflows. Exposure to MLOps tools such as MLflow, Kubeflow, or Azure ML. ...

scipy, scikit-learn, TensorFlow or PyTorch. Experience working across cloud environments (GCP, AWS, or Azure) and analytics platforms such as Dataiku. Good understanding of MLOps practices, including deployment, monitoring, and retraining pipelines. Proven ability to work cross-functionally with marketing, CRM, product, and engineering teams. Excellent communication, leadership, and stakeholder ...

analytical, data science and AI projects. - Build, maintain and optimise data pipelines, analytical models and data architecture. - Develop and operationalise machine learning solutions, applying MLOps best practice. - Produce high-quality, accessible dashboards and tools using platforms such as Power BI. - Work closely with senior leaders to shape strategic insight, policy ...

multi-supplier environment. Familiarity with data science tooling and terminology (Python, SQL, etc.) is desirable. Understanding of cloud platforms (Azure, AWS, GCP) and MLOps and DevOps practices. Demonstrable experience working as a Business Analyst in large complex organisations, preferably within a consultancy. Benefits: Hybrid working model allowing flexibility. Opportunity ...

explain clearly to non-technical audiences DS background that has moved into AI engineering/LLM product delivery is ideal Bonus: Databricks, Azure, MLOps/CI-CD experience Interested? Please apply below. ...

solid understanding of machine learning fundamentals and data workflows 💬 Strong communication skills and a collaborative mindset ⭐ Big bonus points for: ⚙️ Exposure to ML pipelines, MLOps, or production ML systems 📦 Experience with tools such as Databricks, Snowflake, or similar platforms 🔍 Familiarity with version control, testing practices, or CI/CD workflows ...

skills and experience with ML frameworks (e.g. scikit-learn, TensorFlow, PyTorch). Experience with model deployment, monitoring and ML pipelines (e.g. CI/CD, MLOps concepts). A collaborative mindset with strong communication skills and experience working in cross-functional teams. If this role looks of interest, apply below. ...

Cloud Functions/Cloud Run Solid Python experience for ML and data engineering workloads Experience productionising models (not just experimentation or notebooks) Understanding of MLOps concepts: CI/CD, monitoring, retraining, and model governance Ability to work independently in a contract environment and deliver at pace Nice to Have Experience ...

explain clearly to non-technical audiences DS background that has moved into AI engineering/LLM product delivery is ideal Bonus: Databricks, Azure, MLOps/CI-CD experience Interested? Please apply below. ...

data workflows 💬 Strong communication skills and a collaborative mindset ⭐ Big bonus points for: ❄️ Experience with Snowflake 🧱 Experience with Databricks ⚙️ Exposure to ML pipelines, MLOps, or production ML systems If you’re excited about building AI systems, learning fast, and growing your career in a supportive, high-impact environment ...

Databricks, LangChain , vector databases , or LLM orchestration tools . Knowledge of RAG pipelines , transformers , and modern AI architectures . Understanding of deployment or MLOps best practices (Azure experience a plus). Recent, hands-on experience with applied LLM projects . Strong communication and stakeholder engagement skills. A Master’s degree ...

hypotheses through well-designed ML experiments. Hands-on experience creating and deploying AI/ML models , including Large Language Models (LLMs) . Understanding of MLOps principles and deploying models into production environments. Collaboration & Delivery Experience working within - and ideally leading - cross-functional teams . Ability to translate complex business challenges ...

human-in-the-loop review where needed. Ensure regulatory alignment (e.g., GDPR, ISO controls) and collaborate with risk, legal, and security for approvals. Define MLOps processes for versioning, evaluation, promotion, rollback, and monitoring (latency, cost, drift, hallucination rate, safety events). Instrument observability (logging, tracing, metrics) and error budgets; establish ...

MLOps Engineer - Azure ML, Github Up to £550pd (Inside IR35) London/Hybrid 6 months My client is looking for an MLOps Engineer to build and maintain scalable, automated AI systems on Azure. You'll develop CI/CD pipelines for AI/ML, manage model deployment and monitoring … work closely with data scientists and engineers to ensure secure, reliable, and compliant AI operations across environments. Key Requirements: Proven experience in MLOps with strong focus on Azure Proficient in Azure Machine Learning, Cognitive Services, Speech Services, Azure OpenAI Hands-on with Azure DevOps, Azure CLI, and CI/ ...

MLOps Data Engineer Hybrid 2/3 days London Outside IR/35 Were looking for a Data Engineer with strong MLOps ownershipsomeone who builds reliable data pipelines and designs, runs, and improves ML pipelines in production. You wont be training models day-to-day like a Data Scientist; instead … packaging, deployment, and retraining Partner closely with Data Scientists to productionize models: standardise workflows, ensure reproducibility, and reduce time-to-production Build and maintain MLOps automation: CI/CD for ML, environment management, artefact handling, versioning of data/models/code Implement observability for ML systems: monitoring, alerting, logging ...

stakeholders. At least 3 years of experience in AI solution engineering. Large Language Models experience including prompt engineering and RAG implementations. Expert data analytics, MLOps practices and API development. Desirable knowledge in Docker and Kubernetes ...

SaaS architecture. Solid ETL, data processing, and feature engineering experience. Strong statistical and analytical capabilities. Experience in start-up or scale-up environments. Desirable MLOps tools (MLflow, Kubeflow, Sagemaker, etc.) Experience in PropTech or property data. Cloud experience (AWS/GCP/Azure). Docker/Kubernetes, CI/ ...