26 to 50 of 68 MLOps Jobs in London

Architecture Govern technical decisions around cloud compute, security, networking, and DevOps practices. Ensure cloud-native scalability, monitoring, and resilience of the AI-driven quoting platform. Oversee the adoption of MLOps pipelines (i.e., HeatWave AutoML + OCI Data Science). Define architectural standards and enforce architecture reviews across all technical workstreams. Governance & Leadership Run the Architecture Review Board (ARB) for the More ❯

architectures. Providing technical leadership to drive up levels of technical expertise and best practice across the Machine Learning discipline, leading by example and mentoring others. Working closely with our MLOps team to steer the ongoing development of tools to enable rapid iteration of models and optimisations of the full ML model lifecycle. You should apply if What we're doing More ❯

Build and scale pricing models to drive revenue and customer value Lead experimentation (A/B, MAB, causal inference) and analytics projects Set best practices in data science and MLOps Mentor and manage a UK/US-based team Communicate insights and partner with product, engineering, and commercial teams What We're Looking For 10+ years in data science; 5+ More ❯

as OpenAI, LangChain/LangGraph, LlamaIndex Experience with Hugging Face and LoRA/QLoRA for fine-tuning Experience with RAG & Vector DBs eg. FAISS, Weaviate, Pinecone Any experience of MLOps with MLFlow, AWS (SageMaker), CI/CD (GitHub Actions) or similar would be a benefit to an application The employer is well known not only for the forward-thinking approach More ❯

ML architecture and system design (minimum 2 years) with proven expertise in hands-on coding AI solutions. Deep knowledge of machine learning frameworks, cloud platforms (AWS, Azure, GCP), and MLOps(machine learning operations) practices. Deep knowledge about design and implementation of secure integrations between AI solutions and legacy enterprise software, whilst ensuring compliance with government-level security requirements, implementing single More ❯

a related field Experience with multi-modal models that combine vision and language Strong grasp of data-centric AI practices - annotation tooling, prompt evaluation, and dataset curation Familiarity with MLOps tools (e.g. Weights & Biases, SageMaker, MLflow) Experience working in regulated sectors like insurance, banking, or property What You'll Be Doing This is a hands-on, high-impact role - you More ❯

maintain secure AWS cloud infrastructure Implement and manage CI/CD and DevOps pipelines Champion data quality, security, and best practices Collaborate with cross-functional teams Implement and manage MLOps capabilities Essential Skills: Advanced Python programming skills Expertise in data engineering tools and frameworks (Apache Flink) Hands-on AWS experience (Serverless, CloudFormation, CDK) Strong understanding of containerization, CI/CD More ❯

reinforcement learning Model selection, training, validation, and evaluation Deep learning architectures (CNNs, RNNs, Transformers) Data Science Statistical analysis, feature engineering, A/B testing Experiment design and hypothesis testing MLOps & Engineering Scalable ML systems (batch and real-time) ML pipelines, CI/CD, monitoring, deployment Familiarity with tools like MLflow, Kubeflow, Airflow, Docker, Kubernetes Strategic skills Align ML initiatives with More ❯

ML architecture and system design (minimum 2 years) with proven expertise in hands-on coding AI solutions Deep knowledge of machine learning frameworks, cloud platforms (AWS, Azure, GCP), and MLOps(machine learning operations) practices. Deep knowledge about costing of AI models to produce reasonable ROI and modular reusable services with moderate level of maintenance Understanding of data architecture, API design More ❯

ML architecture and system design (minimum 2 years) with proven expertise in hands-on coding AI solutions. Deep knowledge of machine learning frameworks, cloud platforms (AWS, Azure, GCP), and MLOps(machine learning operations) practices. Deep knowledge about design and implementation of secure integrations between AI solutions and legacy enterprise software, whilst ensuring compliance with government-level security requirements, implementing single More ❯

ML architecture and system design (minimum 2 years) with proven expertise in hands-on coding AI solutions Deep knowledge of machine learning frameworks, cloud platforms (AWS, Azure, GCP), and MLOps(machine learning operations) practices. Deep knowledge about costing of AI models to produce reasonable ROI and modular reusable services with moderate level of maintenance Understanding of data architecture, API design More ❯

these AI products. You will be building tools for model training and evaluation, collaborating with Data Scientists and Engineers to get these solutions into production, and driving improvements in MLOps processes. You'll have worked as a Machine Learning Engineer or Data Scientist in the past, ideally centred around a software product, and have solid Python coding skills, and expertise More ❯

haves Python wizardry and strong PyTorch . Hands-on experience with Audio/Video AI - HuggingFace Transformers, SpeechBrain or torchaudio, one detection/pose stack (YOLO, MediaPipe). Production MLOps: experiment tracking, model registries, CI/CD, model monitoring. Comfortable with GCP, containers, and GPUs. Located in London or ready to relocate . Nice to haves Real-time inference experience More ❯

and scaling cross-functional teams (Data Science, ML Engineering, Software Engineering, Managers). Defining the ML/AI roadmap and aligning it to business outcomes. Driving best practice in MLOps, deployment, observability, and automation. Working with cutting-edge approaches: deep learning frameworks, foundation models, knowledge graphs, etc. Being the voice of ML/AI leadership, setting standards, mentoring, and building More ❯

or prototypes at high velocity, ideally in startup or research contexts where speed and adaptability are paramount. Deep expertise in Generative AI, multi-agent systems, LLMs, end-to-end MLOps, and AI infrastructure. Exposure to Deep Learning, Reinforcement Learning, federated learning, AI evaluation or ML fundamentals is highly beneficial. Active interest and awareness of SOTA in multi-agent systems, collective More ❯

as a C# Developer with proficiency in Python, .NET framework and C# as well as experience with ML.NET, ASP.NET, MVC, Web API Containers (Docker/Kubernetes), Machine Learning Operations (MLOps) and CI/CD Good knowledge of IT industry trends (especially GenAI and AgenticAI), suppliers, platforms and products Post productions MLOps, Model retaining and Drift monitoring in Azure with knowledge More ❯

initiatives. Working for a US Sports analytics client you'll collaborate with their technical team, providing advanced mathematical models derived from large datasets and statistics for the client's MLOps team to productionise. Location/WFH: You'll join a friendly and sociable team in the London office two days a week (Mondays and Thursdays) with flexibility to work from More ❯

initiatives. Working for a US Sports analytics client you'll collaborate with their technical team, providing advanced mathematical models derived from large datasets and statistics for the client's MLOps team to productionise. Location/WFH: You'll join a friendly and sociable team in the London office two days a week (Mondays and Thursdays) with flexibility to work from More ❯

initiatives. Working for a US Sports analytics client you'll collaborate with their technical team, providing advanced mathematical models derived from large datasets and statistics for the client's MLOps team to productionise. Location/WFH: You'll join a friendly and sociable team in the London office two days a week (Mondays and Thursdays) with flexibility to work from More ❯

large (AI) transformational journeys BCG does for its clients. Often involves the following engineering disciplines : Cloud Engineering Data Engineering (not building pipelines but designing and building the framework) DevOps MLOps/LLMOps Often work with the following technologies : Azure, AWS, GCP Airflow, dbt, Databricks, Snowflake, etc. GitHub, Azure DevOps and related developer tooling and CI/CD platforms, Terraform or … other Infra-as-Code MLflow, AzureML or similar for MLOps; LangSmith, Langfuse and similar for LLMOps The difference to our "AI Engineer" role is: Do you "use/consume" these technologies, or are you the one that "provides" them to the rest of the organization. What You'll Bring TECHNOLOGIES: Programming Languages: Python Experience with additional programming languages is a More ❯

Job Title: ML Ops/LLM Ops Engineer Location: Hybrid (potentially 1 day per week in east London) Contract: 6 month Contract Day Rate: £400 per day (Outside IR35) About the Role We are seeking an experienced ML Ops/ More ❯

initiatives. Working for a US Sports analytics client you'll collaborate with their technical team, providing advanced mathematical models derived from large datasets and statistics for the client's MLOps team to productionise. Location/WFH: You'll join a friendly and sociable team in the London office two days a week (Mondays and Thursdays) with flexibility to work from More ❯

initiatives. Working for a US Sports analytics client you'll collaborate with their technical team, providing advanced mathematical models derived from large datasets and statistics for the client's MLOps team to productionise. Location/WFH: You'll join a friendly and sociable team in the London office two days a week (Mondays and Thursdays) with flexibility to work from More ❯

initiatives. Working for a US Sports analytics client you'll collaborate with their technical team, providing advanced mathematical models derived from large datasets and statistics for the client's MLOps team to productionise. Location/WFH: You'll join a friendly and sociable team in the London office two days a week (Mondays and Thursdays) with flexibility to work from More ❯

cloud data. Build end-to-end data pipelines : collect sim/tele-op logs, manage versioned data lakes, apply weak supervision, curate datasets, and run evaluation loops. Collaborate with MLOps and Data Platform teams to scale distributed training and optimise models for real-time deployment . Hire, mentor, and lead a high-calibre team of research scientists and engineers. What More ❯