1 to 25 of 63 Permanent MLOps Jobs in London

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 ...

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 ...

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 ...

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. ...

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 ...

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/ ...

product selling. Demonstrated success in selling: Generative AI and LLM-based enterprise solutions Predictive and prescriptive analytics Intelligent Automation (RPA + AI) Data Engineering, MLOps, and AI platforms Ability to articulate AI ethics, governance, security, and compliance considerations. Experience selling Managed AI Services and AI-as-a-Service models . ...

platforms, frameworks, and libraries 📚 Extensive experience with end-to-end model design and deployment within cloud environments ☁️ A systems thinking approach 🌐, with passion for MLOps best practises 🌀 An engineer that can think in O(n) as much as plan the orchestration of their product. Solid understanding of data structures, data ...

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 ...

Lead MLOps Engineer - London - Permanent London, UK (If you like the sound of this role and want to relocate - the Client is willing to help facilitate this move!) This is a high-impact role within a fast-growing AI and robotics organisation focused on building advanced, scalable intelligent systems … real-world industrial applications. The position owns the machine learning infrastructure and MLOps foundations as products, platforms, and teams scale. You will play a key role in transforming machine learning prototypes into reliable production systems, defining pragmatic engineering standards, and enabling fast, safe delivery of ML-powered capabilities. The role ...

globally. Key Responsibilities Research and develop state-of-the-art ML algorithms for network orchestration Build and manage ML training infrastructure using Kubernetes and MLOps tools Develop and maintain documentation for new algorithms and systems Integrate AI/ML solutions into the wider Spacetime platform Act as a technical expert ...

unblock compute and data challenges What We’re Looking For Strong experience in one or more of: Platform/infrastructure engineering ML infrastructure or MLOps HPC or GPU compute Data engineering at scale Solid experience with Linux and cloud environments Hands-on work with Kubernetes or distributed systems Experience with ...

language models including prompt engineering, fine-tuning and evaluation Practical experience with core Generative AI frameworks and agent-based AI frameworks Strong experience with MLOps and LLMOps tooling including model lifecycle management Proven deployment experience on major cloud platforms including AI and ML services Solid foundation in software engineering principles ...

engineers, ML engineers, architects, and business teams to shape ML initiatives. Present insights clearly through strong data visualisation and storytelling. Uphold software engineering and MLOps best practices (testing, versioning, quality, automation). Contribute to governance, responsible model usage, and data quality standards. Mentor juniors and support code reviews. Required Experience ...

engineers, ML engineers, architects, and business teams to shape ML initiatives. Present insights clearly through strong data visualisation and storytelling. Uphold software engineering and MLOps best practices (testing, versioning, quality, automation). Contribute to governance, responsible model usage, and data quality standards. Mentor juniors and support code reviews. Required Experience ...

CUDA and optimising highly parallelised GPU-based computations are a plus Familiarity with the end-to-end supervised training of neural networks and accompanying MLOps ecosystem are a plus Familiarity with Django and related frameworks/tools Familiarity with other programming languages and frameworks At a personal level ...

high-impact use cases in partnership with Wealth, Enablement and Technology teams. Building, mentoring and developing a high-performing Data Science team. Embedding modern MLOps practices including CI/CD, model monitoring, feature stores and version control. Working closely with Data Engineering, Architecture and Operations teams to ensure scalable, secure … PyTorch, TensorFlow). Demonstrable experience delivering end-to-end ML and GenAI solutions (including RAG, LLMs, embeddings and vector databases). Strong knowledge of MLOps tooling and practices (MLflow, CI/CD, containerisation, monitoring). Experience leading, mentoring and developing technical teams. If you'd like to learn more, please ...