276 to 300 of 409 MLOps Jobs in London

Senior ML Ops Engineer London (Hybrid, 1-2 days per week) | £75,000 - £85,000 + 10% Bonus This is an opportunity to join a growing AI and data organisation that is using advanced analytics ...

Face Transformers, timm, scikit‐learn, NumPy, pandas, OpenCV. Production experience on AWS, specifically Amazon SageMaker: training and processing jobs, pipelines, model registry, endpoints. Practical MLOps: experiment tracking and model versioning with MLflow, hyperparameter optimisation, and reproducible, configuration‐driven training runs. Containerisation and infrastructure as code: Docker and CUDA‐based ...

Face Transformers, timm, scikit-learn, NumPy, pandas, OpenCV. Production experience on AWS, specifically Amazon SageMaker: training and processing jobs, pipelines, model registry, endpoints. Practical MLOps: experiment tracking and model versioning with MLflow, hyperparameter optimisation, and reproducible, configuration-driven training runs. Containerisation and infrastructure as code: Docker and CUDA-based ...

design, deploy and maintain production ML pipelines powering personalised rankings across search results and recommendations. You will collaborate with ML Scientists, Backend Engineers and MLOps to build scalable ranking models, improve latency, observability and real-time serving, and help harden the ML infrastructure for ranking systems. #J-18808-Ljbffr ...

institutions, and enterprises with demanding GPU workloads. Role requires deep ML/infra knowledge, hands‐on Kubernetes, and the ability to discuss model training, MLOps, and hardware considerations with credibility. Up to 40% travel expected. #J-18808-Ljbffr ...

built, governed, and evaluated across the Global Public Sector team. You will define standards for responsible AI, model governance, and production MLOps used by multiple engineers and clients. You will architect high-risk AI systems, lead incident resolution related to model safety and data integrity, and coach senior engineers while ...

models with cloud services to deliver production-ready outcomes. The role spans the full lifecycle, from problem framing and data assessment to model selection, MLOps pipelines, validation, and collaboration with clients and cross-functional teams to drive measurable business value. #J-18808-Ljbffr ...

commercial outcomes with executive leadership. You will manage 100+ engineers across distributed pods, ensure platform modernization, and drive value with real-time analytics, MLOps, and GenAI integration. #J-18808-Ljbffr ...

engine for ranking, retrieval, and recommendations. You will define the ML strategy, build scalable systems, and mentor a high-performing team across machine learning, MLOps, and experimentation. You will drive multi-task models, graph-aware representations, and agentic AI approaches to improve user search, discovery, and itineraries at scale within ...

TokenHub and MCP ecosystem, working with enterprise customers and cross-functional teams. Required are 2+ years in Gen AI/LLMs, strong cloud and MLOps experience, and excellent English. Mandarin is a plus for collaboration with China HQ. This role focuses on architecture, delivery, and customer success across enterprise accounts. ...

senior technical voice for the most sensitive AI decisions our clients face. Your work will define standards for responsible AI, model governance, and production MLOps, architect high-risk systems, and build reusable capabilities that enable multiple teams to ship safer AI at scale. #J-18808-Ljbffr ...

complex domain-specific problems. You will explore cutting-edge architectures and rapidly translate theoretical breakthroughs from arXiv into functional, high-performance prototypes. Collaborating with MLOps, you will implement data strategies, run rigorous experiments, optimise compute and memory usage, and ensure smooth hand-off into scaled production. #J-18808-Ljbffr ...

Evaluate and apply emerging ML and deep learning techniques Key Skills Required: Machine Learning/Deep Learning Recommendation & Personalisation Systems Data Pipelines & Feature Engineering MLOps & Production Deployment A/B Testing & Experimentation Python/ML Frameworks Large-scale Structured & Unstructured Data To be Considered: Please either apply by clicking online ...

Reliably fitting models with complex job dependency graphs Robustness in production, including validation and monitoring Qualifications An appreciation of good architecture and MLOPs best practice The ability to collaborate with, and influence, technical and non‐technical people A passion for end‐to‐end ownership of solutions, from articulation to delivery ...

Senior MLOps Engineer London (Hybrid) | £75,000 - £85,000 + Bonus + BenefitsThis is an opportunity to take ownership of the infrastructure powering a growing AI platform. You'll work across machine learning, NLP, and emerging AI technologies, helping build scalable systems that support real-world impact. The Company … making and deliver better outcomes. They are investing heavily in their AI platform and expanding their engineering capability. The Role Own and develop the MLOps infrastructure supporting AI services. Build and maintain orchestration pipelines using Dagster, Airflow, or Prefect. Deploy and manage ML workloads on Kubernetes and cloud platforms. Implement ...

Software Engineers, and Product teams to deliver scalable solutions Contribute to the development of greenfield machine learning products and capabilities Drive best practices across MLOps, monitoring, observability, and model lifecycle management Your Skills & Experience Strong commercial experience within Machine Learning Engineering, Data Science, or MLOps Experience deploying, monitoring, and maintaining ...

that feed our dispute systems. Collaborate with platform and backend engineers to integrate models seamlessly. Experience and qualifications 5+ years of experience as an MLOps/ML Engineer. High proficiency in writing clear, production‐ready Python code. Experience with production ML models (online or offline) and standard MLOps practices. Experience ...

hallucination and model risk Supporting the move towards increasingly high‐risk and high‐complexity AI/ML use cases Establishing best practice across MLOps, CI/CD and deployment Influencing senior engineers and technical stakeholders without direct line management Helping engineering teams use AI‐assisted development tools effectively within their … Learning/AI engineering AWS Agentic AI and LLM‐based applications Retrieval Augmented Generation (RAG) LangChain and/or LangGraph Vector databases and retrieval MLOps and production deployment AI evaluation, testing and observability AI governance, model risk and engineering controls ML frameworks such as PyTorch, TensorFlow or Scikit‐learn Experience ...

Experience fine‐tuning LLMs (e.g., LoRA, QLoRA, RLHF) specifically for domain‐specific enterprise applications. Taken responsibility of models from concept to production, utilising strong MLOps and LLMOps practices to ensure scalable, reliable, and monitorable deployments. High proficiency in Python and modern software development practices, including rigorous testing, code reviews … Computer Science, Artificial Intelligence, Statistics, Data Science, or a related quantitative field. Experience working with scalable cloud infrastructure (AWS, GCP, or Azure) and MLOps tools for model training, monitoring, and deployment. Background in forecasting, planning, or analytics applications. Familiarity with Anaplan or similar enterprise planning platforms. Experience with A/ ...

/B tests, owning statistical validity and turning results into recommendations teams can act on Collaborate with Engineers to ship models via reproducible MLOps workflows: experiment tracking, model serving, alerting, and production monitoring Communicate model choices, limitations, and trade-offs clearly to both engineers and non-technical stakeholders 3+ years … comfortable with large transactional datasets and common DS/ML libraries (e.g. pandas, scikit-learn, XGBoost, PyTorch or similar) Experience collaborating on MLOps-style workflows (experiment tracking, serving, monitoring) and shipping with engineers Able to communicate uncertainty and model limitations clearly to technical and non-technical audiences Bachelor ...

deliver end-to-end data pipelines. Optimise performance, reliability, and cost-efficiency across all data workflows and systems. Lead development of Superscript’s MLOps capabilities, ensuring seamless model deployment, monitoring, and feedback loops. Own and improve data quality frameworks and observability to ensure trust in business-critical data assets. Produce … frameworks, version control (Git), and containerisation (Docker/Kubernetes). Strong background in data modelling, warehousing, and supporting analytical and ML workloads. Exposure to MLOps tools including MLFlow, kedro and Amazon Sagemaker. Development and maintenance of real time products like APIs using FastAPI/Flask. Operates autonomously with strong ownership ...

deployment. Drive innovation in machine learning algorithms, neural networks, and emerging AI technologies including generative AI and multi‐modal systems. Oversee the implementation of MLOps pipelines, model monitoring, and automated deployment systems. Team Leadership & Mentorship: Lead and mentor a team of AI engineers, data scientists, and data engineers. Foster … Excellence: Proven experience building production‐scale AI systems with focus on performance, reliability, and maintainability. Expertise in software architecture, distributed systems, and DevOps/MLOps practices. Leadership & Collaboration: Demonstrated ability to lead technical teams, drive consensus on technical decisions, and collaborate effectively across departments. Experience mentoring engineers and fostering technical ...

/B tests, owning statistical validity and turning results into recommendations teams can act on Collaborate with Engineers to ship models via reproducible MLOps workflows: experiment tracking, model serving, alerting, and production monitoring Communicate model choices, limitations, and trade‐offs clearly to both engineers and non‐technical stakeholders What … comfortable with large transactional datasets and common DS/ML libraries (e.g. pandas, scikit‐learn, XGBoost, PyTorch or similar) Experience collaborating on MLOps‐style workflows (experiment tracking, serving, monitoring) and shipping with engineers Able to communicate uncertainty and model limitations clearly to technical and non‐technical audiences Bachelor ...

hybrid SQL/Python architecture (dbt, Snowflake) to extract rich, localized temporal signals from high-volume transactional retail data. End-to-End Deployment (MLOps): Partner with our engineers to transition your models from prototype into scalable, monitored, and reliable production pipelines using tools like MLflow and Optuna. Your Skills Strong … transformation tools like dbt. Cloud & Data Infrastructure: Experience working with scalable cloud infrastructure (AWS, GCP, or Azure), and handling columnar data formats like Parquet. MLOps Exposure: Experience with tools and frameworks for training, tracking, deploying, and monitoring models in production (e.g., MLflow, Optuna, Prefect, or Airflow). Background in supply ...

part of the Data and AI platform team; you’ll get to pioneer on real world problems, bringing your knowledge of AI/ML, MLOps and LLMs to bear - collaborating cross team to make your vision a reality. You’ll be backed by our platform team, and have a wealth … scientific evaluation with the operational maturity to champion high-value applications and confidently push back on unsuitable AI use cases. Design, refine and build MLOps component of the data and AI platform, from Vector Databases through feature stored and model serving, all at the millisecond scale. Implement CI/ ...