1 to 25 of 73 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 ...

Search , and custom vector stores. Engineer domain-tuned embeddings using ADA-002, Gecko, Word2Vec, BERT, Sentence Encoder, and topic modelling. AI/ML Pipelines & MLOps Develop scalable AI/ML microservices using Docker, Kubernetes (EKS/GKE), and CI/CD-driven automation. Build and enhance pipelines for model evaluation ...

solutions based on real user feedback and real-world constraints. Learn modern tooling and practices Gain hands-on experience with ML frameworks, cloud infrastructure, MLOps tools, and best practices for building scalable AI systems. Communicate clearly Translate technical findings into clear, structured insights for collaborators across technical and business teams. ...

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 ...

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 ...

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 ...

programming skills Hands-on experience with PyTorch Experience with Hugging Face transformers or similar frameworks Solid experience deploying ML solutions on AWS Understanding of MLOps, model deployment and performance optimisation Ability to work in a consultancy environment with multiple clients Desirable: Experience with LLMs, RAG pipelines or generative AI applications ...

methodology, and statistical evaluation Proven track record transitioning models from experimentation to production with appropriate governance and quality controls Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, Data Version Control) Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures It Would Be Great ...

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. ...

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. ...

experience with ML frameworks (scikit‐learn, XGBoost, PyTorch, TensorFlow, LightGBM). Strong exposure to LLMs, prompt engineering, RAG, and evaluation frameworks. Familiarity with MLOps tooling: CI/CD, experiment tracking, model registries, monitoring, containers. Please note: This role cannot provide VISA sponsorship . ...

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 ...

every stage. Lead initiatives that drive testability, platform resilience, and validation as code across all layers of the ML platform stack. Collaborate with engineering, MLOps, and infrastructure teams to embed quality engineering deeply into platform components. Build reusable components that support scalability, modularity, and self-service quality tooling. Mentor junior ...

every stage.• Lead initiatives that drive testability, platform resilience, and validation as code across all layers of the ML platform stack.• Collaborate with engineering, MLOps, and infrastructure teams to embed quality engineering deeply into platform components.• Build reusable components that support scalability, modularity, and self-service quality tooling.• Mentor junior ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

associated architecture patterns Experience designing and managing APIs including REST and GraphQL Strong understanding of microservices and serverless architectures Practical experience with DevOps and MLOps practices including CI/CD pipelines Knowledge of security frameworks, identity management and authentication standards Desirable Skills for the AI Solutions Architect: Experience with enterprise ...

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/ ...