451 to 475 of 604 MLOps Jobs in England

implementation leadership for major features and platform initiatives, including writing and reviewing critical-path code where the work demands it. Define and oversee the MLOps strategy spanning model training, deployment, monitoring, and lifecycle management on Kubernetes across AWS, GCP, and Azure. Establish and evangelize engineering best practices across services, including … experience with Kubernetes and at least one major cloud provider (AWS, GCP, or Azure), with working familiarity across multi-cloud environments. Demonstrated experience with MLOps tooling and patterns, including model registries, experiment tracking, and CI/CD for. Proven ability to lead architectural strategy across multiple teams and services, balancing ...

more cost-effective to ship than the last. Working closely with our Data Scientists and Data Engineers, you’ll help shape how we do MLOps at PureGym, bringing strong software engineering practices into the modelling lifecycle and making it easier for the team to get great models into production. What … Turn models and metric definitions created by Data Scientists into robust, reusable production components on our shared semantic layer. Help define and evolve our MLOps standards, covering versioning, model registry practices, documentation and handover. Partner with Data Engineers on upstream pipelines and Data Scientists on model design, ensuring production requirements ...

institutional sponsorship enables Radiant to pursue large-scale GPU and AI infrastructure opportunities globally. Job Summary: As a Senior/Principal Product Manager - MLOps, you will own the strategy, roadmap, and delivery of Radiant’s machine learning platform and suite of AI/ML cloud services. You’ll work across … build, train, deploy, and operate ML workloads on Radiant’s neocloud platform. Key Responsibilities: Own the strategy, roadmap, and delivery of Radiant’s MLOps and ML platform services. Define products across the ML lifecycle, including training, fine-tuning, model management, deployment, inference, and monitoring. Translate customer needs into clear product ...

About H: H exists to push the boundaries of superintelligence with agentic AI. By automating complex, multi-step tasks typically performed by humans, AI agents will help unlock full human potential. H is hiring the ...

We recognise our people are the best thing about Red Badger and that a truly diverse workplace where people are free to be themselves is essential for great culture and great outcomes. If you like ...

seeking an AI Engineer in London to design, build and deploy machine learning solutions for enterprise clients, spanning model selection, data pipelines, DevOps/MLOps and production-grade AI services. You will work with Python, C++, and frameworks like TensorFlow and PyTorch, on cloud platforms and scalable data architectures, with ...

engineering industry, delivering the performant, reliable, and trustworthy data foundation trusted by the world’s largest engineering companies. About The Role As an MLOps Engineer on the Physical AI team, you will serve as the hands‐on owner for our machine learning operations surface across the end‐to‐end model … lifecycle—from experimentation and training through to packaging, deployment, serving, and retirement. You will define and roll out MLOps practices, establish operational SLOs/SLAs, and build automated CI/CD and continuous training pipelines to accelerate the path from experiment to supported production deployment. In this role, you will ...

seeking an AI Platform & Site Reliability Engineering Managing Consultant to design, build and scale secure AI platforms. You will integrate SRE, observability and MLOps, guiding senior clients from strategy to delivery. This role offers flexible hybrid working across Manchester, Glasgow or London, with leadership of multidisciplinary teams and governance shaping ...

connected products and industrial IoT capabilities. Initially focus on DevOps, cloud operations, IoT data flows, deployment automation and environment management, with a path into MLOps and ML platform engineering as capability matures. #J-18808-Ljbffr ...

seeking an AI Platform & Site Reliability Engineering Managing Consultant to design, build and scale secure AI platforms. You will integrate SRE, observability and MLOps, guiding senior clients from strategy to delivery. This role offers flexible hybrid working across Manchester, Glasgow or London, with leadership of multidisciplinary teams and governance shaping ...

strengthen platforms that support data, AI, and automation across the organisation. You will guide standards, coach colleagues, and help drive DataOps, AIOps and MLOps improvements while working in a hybrid setup with 50% home/office. #J-18808-Ljbffr ...

deploy AI systems across edge and embedded environments, collaborating with software, hardware and systems teams. The role covers model training, evaluation, optimization, and MLOps, with an emphasis on production-ready solutions. A hybrid work model is offered, with at least 3 days on-site in Bristol and eligibility ...

Tasks within this role Qualification requirements Skills Critical thinking AIOps Natural language processing (NLP) Analytical thinking R (programming language) Problem-solving Programming language Python MLOps Predictive analytics Innovative thinking Job ID: 5354 Location: Milton Keynes, GB, MK14 5AN GB Milton Keynes, GB, MK8 8DF Department: IT and Digitalization Working Model ...

. You will bridge the gap between complex enterprise data strategies—spanning Cloud Migrations, Data Mesh architectures, Real-Time Analytics, and Generative AI/MLOps pipelines—and precise execution across globally distributed engineering pods of 100+ people. The Impact By pairing technical depth with executive gravitas, you will de-risk … Fluency: Deep understanding of modern data stacks (Data Lakehouses, Data Mesh, Real-time Streaming, ETL/ELT pipelines) and AI/ML lifecycle management (MLOps, GenAI integration, Model Governance). Ability to challenge architectural patterns and supervise technical teams without acting as the hands-on architect. Cloud & Platform Mastery: Expertise ...

Analysis: Apply established data science and machine learning techniques to analyse data and solve business problems under the guidance of senior team members. Production & MLOps: Build, test, and help deploy machine learning models into production environments. Contribute to the design and maintenance of MLOps pipelines to ensure solutions are scalable ...

Analysis: Apply established data science and machine learning techniques to analyse data and solve business problems under the guidance of senior team members. Production & MLOps: Build, test, and help deploy machine learning models into production environments. Contribute to the design and maintenance of MLOps pipelines to ensure solutions are scalable ...

data quality, governance, and reliability. Develop multimodal AI models that combine visual, textual, and customer data to deliver enhanced automation and personalisation capabilities. Apply MLOps best practices across the AI lifecycle, including CI/CD, monitoring, model deployment, testing, and performance optimisation. Collaborate with product, engineering, and cloud teams … Prompt Engineering, Retrieval Augmented Generation (RAG), and model fine-tuning techniques. Understanding of modern software engineering practices including Git, CI/CD, Containerisation, and MLOps methodologies. Experience delivering end-to-end machine learning projects from concept through to production deployment. Strong understanding of AI evaluation frameworks, model testing, benchmarking ...

several of the following areas would be advantageous: * Agentic AI * AI Agents & Autonomous Workflows * Generative AI & LLMs * Retrieval Augmented Generation (RAG) * Multi-Agent Systems * MLOps/LLMOps * LangGraph, LangChain, Semantic Kernel, CrewAI or AutoGen * OpenAI Agents, Microsoft Copilot Studio and MCP * Azure, AWS or GCP Education * 2:1 degree … Agentic AI, AI Agents, Generative AI, LLMs, Machine Learning, Data Science, Platform Engineering, LangGraph, LangChain, Semantic Kernel, CrewAI, OpenAI Agents, Microsoft Copilot Studio, MCP, MLOps, RAG, SaaS, Cambridge. ...

customers. They will be subject matter experts in the areas of Predicative AI, Machine Learning, Generative AI along with associated capabilities in areas like MLops, Data Pipelines, and DevOps. The role will interface directly with the customers' business, data science and IT Operations teams to establish CDW credibility and position … learning, lab time and formal training. What you will do Position CDW Custom AI capabilities - Engage with all Customer AI personas: technical personas (CDO, MLOps, AI/ML Engineer, Data Scientist), outcome-driven personas (CEO, CTO, LOB) and ITDMs (CIO, VP Infrastructure). Technical SME on CDW Custom AI – Maintain ...

customers. They will be subject matter experts in the areas of Predicative AI, Machine Learning, Generative AI along with associated capabilities in areas like MLops, Data Pipelines, and DevOps. The role will interface directly with the customers' business, data science and IT Operations teams to establish CDW credibility and position … learning, lab time and formal training. What you will do Position CDW Custom AI capabilities - Engage with all Customer AI personas: technical personas (CDO, MLOps, AI/ML Engineer, Data Scientist), outcome-driven personas (CEO, CTO, LOB) and ITDMs (CIO, VP Infrastructure). Technical SME on CDW Custom AI – Maintain ...

customers. They will be subject matter experts in the areas of Predicative AI, Machine Learning, Generative AI along with associated capabilities in areas like MLops, Data Pipelines, and DevOps. The role will interface directly with the customers' business, data science and IT Operations teams to establish CDW credibility and position … learning, lab time and formal training. What you will do Position CDW Custom AI capabilities - Engage with all Customer AI personas: technical personas (CDO, MLOps, AI/ML Engineer, Data Scientist), outcome-driven personas (CEO, CTO, LOB) and ITDMs (CIO, VP Infrastructure). Technical SME on CDW Custom AI – Maintain ...

Depop is looking for a Machine Learning Engineer to join the Ranking team in the UK. You will work alongside ML Scientists, Backend Engineers, MLOps, and other ML Engineers to build, deploy, maintain, and monitor the machine learning systems that power personalised ranking across key surfaces of the Depop … product teams to define integration requirements and coordinate deployment of ranking services. Help extend the ML infrastructure for ranking systems in collaboration with the MLOps team, including: Reproducible model training workflows CI/CD pipelines for model deployment Real-time and batch model serving Online/offline feature consistency through ...

Azure-based data and AI platform. This is hands‐on infrastructure and platform work – you'll be writing Terraform, designing network architecture, implementing MLOps (AI model) pipelines, and establishing DevSecOps patterns that engineering teams can use. This isn't a “thought leadership” or “strategy” role. … maintain CI/CD for Databricks notebooks, jobs, and infrastructure Work with data engineers on performance optimization, cost control, and platform capabilities MLOps & AI Platform (20%) Build ML model deployment pipelines using Azure ML, Databricks MLflow, or both Implement model versioning, experiment tracking, and model registry patterns Establish inference endpoints ...

including generative AI and agentic systems, on cloud infrastructure. You will own end-to‐end delivery from problem framing and experimental design through scalable MLOps, evaluation, governance, and integration with strategic systems. You will help set technical direction, establish reusable patterns, and ensure solutions are reliable, secure, and observable … reliability, incident management, and continuous improvement Strong Python software engineering skills, including modular object‐oriented design, testing, performance tuning, and debugging Working knowledge of MLOps and LLMOps and distributed systems, including training and serving patterns, batch versus real‐time architectures, feature stores, orchestration, and scalable data processing Ability to design ...

lifecycle — assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and Large Language Models), designing scalable data and DevOps & MLOps pipelines for training and production, and ensuring quality, value, and reliability of deployed systems. THE WORK Formulate real-world problems into practical, efficient, and scalable … Develop and implement machine learning algorithms, models, and computational systems; design and build scalable data pipelines to support model training and production with DevOps & MLOps Customize and apply Deep Learning and Gen AI models for various use cases based on the business needs, data availability, system and infrastructure requirements - including ...