576 to 600 of 707 MLOps Jobs in the UK

issues early and keep things running smoothly Troubleshooting complex issues across distributed systems, spanning data infrastructure, training, and inference Continuously improving our data and MLOps practices, and helping shape the roadmap for how our platform evolves as we scale What you'll need Strong proficiency in Python and SQL, with … quality practices (validation, cleaning, normalisation) and/or production-grade observability Ability to design resilient, scalable architectures, and comfort operating and troubleshooting distributed systems MLOps experience, for example model serving, experiment tracking, GPU/distributed training optimisation, or reproducible ML workflows A self-starter mentality: comfortable identifying problems and driving ...

issues early and keep things running smoothly Troubleshooting complex issues across distributed systems, spanning data infrastructure, training, and inference Continuously improving our data and MLOps practices, and helping shape the roadmap for how our platform evolves as we scale What you'll need Strong proficiency in Python and SQL, with … quality practices (validation, cleaning, normalisation) and/or production-grade observability Ability to design resilient, scalable architectures, and comfort operating and troubleshooting distributed systems MLOps experience, for example model serving, experiment tracking, GPU/distributed training optimisation, or reproducible ML workflows A self-starter mentality: comfortable identifying problems and driving ...

Platform Strategy & Architecture: Assess, define and evolve enterprise AI platform architectures, covering LLM and agentic frameworks, AI gateways, model lifecycle management, data platforms, MLOps/LLMOps foundations and integration patterns. Support clients in evaluating build, buy and hybrid approaches aligned to business needs, risk appetite and operational requirements. AI Platform … operating cloud-native, platform engineering, AI platform or reliability engineering solutions within complex enterprise environments. Strong understanding of AI platform architectures including LLMOps, MLOps, agentic AI frameworks, model lifecycle management and AI operational controls. Experience establishing and scaling SRE practices including observability, SLIs, SLOs, error budgets, incident management and reliability ...

Platform Strategy & Architecture: Assess, define and evolve enterprise AI platform architectures, covering LLM and agentic frameworks, AI gateways, model lifecycle management, data platforms, MLOps/LLMOps foundations and integration patterns. Support clients in evaluating build, buy and hybrid approaches aligned to business needs, risk appetite and operational requirements.• AI Platform … operating cloud-native, platform engineering, AI platform or reliability engineering solutions within complex enterprise environments. • Strong understanding of AI platform architectures including LLMOps, MLOps, agentic AI frameworks, model lifecycle management and AI operational controls. • Experience establishing and scaling SRE practices including observability, SLIs, SLOs, error budgets, incident management and reliability ...

Platform Strategy & Architecture: Assess, define and evolve enterprise AI platform architectures, covering LLM and agentic frameworks, AI gateways, model lifecycle management, data platforms, MLOps/LLMOps foundations and integration patterns. Support clients in evaluating build, buy and hybrid approaches aligned to business needs, risk appetite and operational requirements.• AI Platform … operating cloud-native, platform engineering, AI platform or reliability engineering solutions within complex enterprise environments. • Strong understanding of AI platform architectures including LLMOps, MLOps, agentic AI frameworks, model lifecycle management and AI operational controls. • Experience establishing and scaling SRE practices including observability, SLIs, SLOs, error budgets, incident management and reliability ...

Platform Strategy & Architecture: Assess, define and evolve enterprise AI platform architectures, covering LLM and agentic frameworks, AI gateways, model lifecycle management, data platforms, MLOps/LLMOps foundations and integration patterns. Support clients in evaluating build, buy and hybrid approaches aligned to business needs, risk appetite and operational requirements.• AI Platform … operating cloud-native, platform engineering, AI platform or reliability engineering solutions within complex enterprise environments. • Strong understanding of AI platform architectures including LLMOps, MLOps, agentic AI frameworks, model lifecycle management and AI operational controls. • Experience establishing and scaling SRE practices including observability, SLIs, SLOs, error budgets, incident management and reliability ...

Company Description Depop is the community-powered circular fashion marketplace where anyone can buy, sell and discover desirable secondhand fashion. With a community of over 35 million users, Depop is on a mission to make ...

Join JPMorgan Corporate Investment Bank's industry-leading data analytics team, where you'll combine cutting-edge machine learning techniques with unique data assets to optimize business decisions. As an Applied AI & Machine Learning Associate ...

At Elanco (NYSE: ELAN) – it all starts with animals! As a global leader in animal health, we are dedicated to innovation and delivering products and services to prevent and treat disease in farm animals and ...

We're looking for a hands-on, generalist data scientist to own ML across all parts of our broad business, including Membership, Food & Beverage, Operations, Digital and Events. As Lead Data Scientist, you'll be ...

Thehumanoid is hiring a Senior Dev/MLOps Engineer to join their Data & Compute Platform team in London. This role involves managing the lifecycle of ML models and infrastructure, building CI/CD pipelines, ensuring system reliability and security, and collaborating with cross-functional teams. Candidates should have strong Python ...

from data pipelines to model deployment, collaborating with platform and backend engineers to scale production systems. Applicants should have 5+ years in ML/MLOps, strong Python, Databricks experience, and familiarity with AWS or Azure. #J-18808-Ljbffr ...

more excellent minds to challenge the status‐quo and help it reach new horizons. Where you come in? We're looking for a Senior MLOPs Engineer to join our Algorithmic Underwriting team. In this role, you’ll develop and extend our MLOPs system, empowering our underwriting algorithm to improve effectively … efficiently. We'll work together across the business to tackle exciting technical challenges that go far beyond traditional MLOPs systems. For instance, you’ll expand our MLOPs system to manage the lifecycle of actuarial models and rules‐based models alongside standard machine learning models. You’ll also have the opportunity ...

modern architectures: Medallion Lambda Kappa Experience building or contributing to cloud-based data platforms AWS preferred Familiarity with: S3 Glue Athena Data Science & MLOps Exposure to: data analysis MLOps feature engineering Curiosity and willingness to go deeper in these areas Why you’ll love it here We want to make ...

View all jobs MLOps Engineer London • Remote Engineering Remote • In office Full-time About Boltz Boltz is a public benefit company building the next generation of AI-powered molecular modeling tools to make biology programmable and accelerate drug discovery, while keeping frontier capabilities broadly accessible. Boltz-1, Boltz … iterate faster and stay focused. You can read more about our mission, research and product vision on our manifesto. About the role As an MLOps Engineer, you will focus on optimizing, deploying, and operating large-scale machine learning models that power Boltz Lab. Your primary responsibility will be to ensure ...

trusted to do so with minimal oversight. It's not solo delivery: you'll be working closely with a dynamic team spanning ML, MLOps and software engineering, and should be happy to contribute at every level, from early-stage research through to production support. Role & Core Responsibilities Own the delivery … data quality issues and model degradation as a priority. Research new ML applications and improve pre-existing models, sharing findings with the wider ML, MLOps and engineering team. Collaborate closely with product, MLOps and engineering teams to define and prepare new ML applications, contributing meaningfully to planning and grooming. Proactively ...

Global is seeking an MLOps AI Lead & Databricks Platform Specialist to own the Databricks Lakehouse Platform and drive robust MLOps capabilities across the enterprise. The role involves platform service management, security governance, and collaborating with Data Engineering and Data Science teams to operationalize ML models through CI/CD, testing ...

needs more excellent minds to challenge the status-quo and help it reach new horizons. Where you come in? We're looking for an MLOps Engineer to join our Algorithmic Engineering team. In this role, you’ll help us build and scale our MLOps system, enabling our underwriting algorithm … grow and scale. You’ll collaborate with colleagues across the business to solve complex technical challenges, such as generalising our MLOps system to manage actuarial and rules-based models. You’ll have the autonomy to propose, design, and execute innovative initiatives, delivering high-impact features as part of our forward ...

real-time and batch data Training, deploying, and maintaining machine learning models in production environments Working with Databricks, AWS, Python, Kafka, and modern MLOps tooling Leveraging machine learning, LLMs, and multimodal AI techniques across structured and unstructured data sources Collaborating with engineering, product, and business stakeholders to deliver impactful solutions … technical best practices Driving measurable improvements through AI-powered decisioning and automation Your Skills & Experience Strong commercial experience as a Machine Learning Engineer, MLOps Engineer, or in a similar applied ML role Proven experience building and deploying machine learning systems into production Experience designing and maintaining scalable data pipelines ...

turn it into something dependable enough to run in a live, privacy-sensitive, multi-partner environment. You'll set the ML engineering and MLOps direction for your workstreams and act as technical lead on how research gets built, tested, and shipped. What you'll do Lead the ML engineering … MLOps direction for assigned CODAS workstreams. Design, build, test, and maintain the ML services, data pipelines, evaluation tooling, and deployment-ready applications that make up the programme's technical deliverables. Translate research outputs into systems that are actually robust and maintainable, not just proofs of concept. Adapt AI/ ...

Platform Architect Own the end-to-end architecture of an enterprise AI platform across Azure and AWS. Design capabilities for ML training, model hosting, MLOps and Generative AI. Define architecture patterns for LLMs, RAG, vector search and agent-based frameworks. Evaluate AI technologies and select appropriate tooling based on technical … enterprise-scale AI or ML platforms. Strong architecture experience across Azure and AWS , including multi-cloud integration, networking, IAM and security. Deep understanding of MLOps , model training pipelines, orchestration, feature stores, model hosting and observability. Strong Generative AI architecture experience, including LLM hosting, RAG, vector databases, prompt orchestration and guardrails. ...

Platform Architect Own the end-to-end architecture of an enterprise AI platform across Azure and AWS. Design capabilities for ML training, model hosting, MLOps and Generative AI. Define architecture patterns for LLMs, RAG, vector search and agent-based frameworks. Evaluate AI technologies and select appropriate tooling based on technical … enterprise-scale AI or ML platforms. Strong architecture experience across Azure and AWS , including multi-cloud integration, networking, IAM and security. Deep understanding of MLOps , model training pipelines, orchestration, feature stores, model hosting and observability. Strong Generative AI architecture experience, including LLM hosting, RAG, vector databases, prompt orchestration and guardrails. ...

agent-to-agent communication Design information barriers and authorization for agents acting on behalf of different counterparties, ensuring secure and isolated execution Define MLOps architecture for agent tools, covering training, environment promotion, model registry, serving, monitoring, and retraining cadence Establish engineering standards for agent quality and safety, including evaluation frameworks … more programming languages, with Python required Architect and ship production LLM agents or multi-agent systems Design end-to-end ML platforms or MLOps pipelines Exhibit expertise in secure distributed systems, including identity, authorization, and service-to-service trust Lead with AI-assisted software development tools and validate AI outputs ...

processing enterprise-scale workloads, including millions of claims per month, while maintaining performance, reliability, scalability, and operational efficiency. Define and implement AI/MLOps strategies covering model monitoring, retraining pipelines, drift detection, automated testing, and continuous improvement. Build and manage evaluation frameworks for agentic AI systems, assessing output correctness, safety … Strong understanding of AI/ML engineering principles, including model development, training, evaluation, validation, deployment, and performance optimization. Experience designing AI/ML and MLOps pipelines, including model monitoring, drift detection, automated retraining, testing, and production operations. Familiarity with AI observability and evaluation practices, including tracing, debugging, safety assessments, latency ...

Science and machine learning across the organisation. Define approaches for model development, validation, deployment and lifecycle management. Develop scalable and repeatable Data Science and MLOps processes within the Azure Databricks Lakehouse environment. Collaborate with Data Engineering, Data Architecture and Data Governance teams to ensure Data Science solutions align with wider … model development. Exposure in developing Data Science solutions within Azure Databricks or similar cloud-based analytics platforms. Proficiency in implementing or working with MLOps practices, including model deployment, version control, monitoring and lifecycle management. Know-how of translating business challenges into practical Data Science solutions and communicating insights ...