176 to 198 of 198 MLOps Jobs in London

Financial Services, Asset Management , or a similarly regulated industry. Technical Proficiency: Hands-on experience with Data Lakes/Warehouses (e.g., Snowflake), Feature Stores, and MLOps concepts. Data Literacy: Ability to perform data profiling and analysis using SQL and Python to drive product decisions. Communication: Exceptional ability to influence a diverse … related field (Master's preferred). Technical Skills Required Data Tools: SQL, Python, Snowflake/Databricks, Tableau/Power BI. Frameworks: Agile/Scrum, MLOps, Data Governance, Metadata Management. Integration: API design, event-driven architecture, and data modeling. Randstad Technologies is acting as an Employment Business in relation to this ...

infrastructure using Microsoft Azure technologies including Azure AI Foundry, Azure OpenAI, Azure ML and Copilot Studio. Develop and manage CI/CD, LLMOps, and MLOps pipelines for Generative AI applications. Design and deploy GenAI, RAG and intelligent agent solutions across enterprise systems. Implement Infrastructure as Code (Terraform) and secure cloud … infrastructure engineering. Hands-on expertise with Generative AI, RAG architectures, NLP and agentic workflows. Experience building secure CI/CD, LLMOps, and MLOps pipelines. Knowledge of Terraform, cloud security, APIs, and enterprise integrations. Familiarity with frameworks such as LangChain or Semantic Kernel. Strong stakeholder communication and cross-functional collaboration skills. ...

This job is with WTW, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. Description This role will primarily focus ...

looking for a Lead Platform Engineer to join a growing engineering organisation and play a pivotal role in designing, building, and operating an MLOps platform that enables AI and data science teams to deliver reliably in production. This is a senior, hands-on technical leadership role , not a people-management … based workloads safely and at scale . The focus is not simply on running Kubernetes clusters - it's on layering real MLOps capability on top of Kubernetes to create a platform that is usable, supportable, and trusted in live environments. What you'll be doing ...

within a multidisciplinary data team, partnering with data engineers, architects, and analysts to design, build, and productionise machine-learning models using modern Python and MLOps practices across secure cloud environments. Key Requirements Active Security Clearance (SC) - must be current and transferable Proven experience as a Data Scientist in central government … Strong hands-on experience with Python for data science and machine learning Solid understanding of machine-learning techniques (supervised, unsupervised, model evaluation) Experience with MLOps practices, including: Model versioning and deployment CI/CD for ML workflows Monitoring and performance management Experience working with cloud-based data platforms ...

making the function known Team Building Scale the team in line with what the roadmap demands – hiring across ML engineering, data science, and MLOps Develop the existing team, create career paths, and set technical and cultural standards What the First Year Looks Like 3 months: DS roadmap defined cross‐functionally … external presence. 12 months: DS is a recognised, embedded function with a track record of delivery. Experimentation is a working discipline used beyond DS. MLOps maturity has stepped up. The team has grown in line with what was needed to get here. What You Bring Must‐Haves 4‐5 years ...

efforts, working with our technical teams to design, build and operate mission-critical machine learning capabilities. This includes everything from infrastructure and MLOps through to training, fine-tuning, deployment, and live model management. While you’ll be hands-on in engineering, your work will also be advisory, ensuring models … embedded models as needs evolve. Hands-on experience working alongside scientists and within deployment environments is central to this role, as is familiarity with MLOps tooling, scheduling, and orchestration. You’ll be comfortable working in cloud environments and with ML lifecycle tools such asWeights & Biases, and you’ll have Python ...

ML Ops Engineer Role Overview Were hiring an ML Ops Engineer to own the reliability, scalability, and operational integrity of our machine-learning systems in research & production. This role sits at the intersection of data ...

We're working with a fast moving tech business looking for a Senior ML Engineer to own personalisation end to end. This isn't a research role. You'll be shipping models to production, building ...

Qureight’s mission is to accelerate clinical trials and ensure breakthroughs in lung and heart disease reach patients without delay. Our AI‐powered data and imaging curation platform enables the analysis of clinical imaging and ...

As the Presales AI Solutions Architect within our AI Practice, you'll play a key role in technological advancement, developing a team and leading successful projects in all UK sectors. You will have the opportunity ...

engineers to build and integrate ML models into tools produced for third-party developers. The ideal candidate has strong programming skills and experience with MLOps/LLMOps, and a relevant degree in Computer Science or a related field. This role offers opportunities for collaboration and innovation. #J-18808-Ljbffr ...

quality into actionable product requirements. Lead products from concept to prototype to production, ensuring scalability, performance, and regulatory alignment. Establish agile, DataOps, and MLOps practices to accelerate deployment of analytics and AI solutions. Guide the integration of operational data across ecosystems (MES, LIMS, ERP, QMS, plant-level data, SCADA … pharma, biopharma, or large-scale manufacturing/supply chain. Strong technical fluency: data engineering & pipelines, cloud platforms, data science & AI/ML product delivery, MLOps/DataOps principles. Skilled at influencing product direction across cross‐functional, multi-region teams. A strategic thinker capable of shaping long-term transformation while executing ...

Protection and Legal, covering model risk, data privacy, transparency, human in the loop and auditing.* Build the target operating model for AI engineering and MLOps, including model lifecycle management, monitoring, incident response, versioning and cost/performance optimisation.* Set global standards for AI platforms, toolchains and integration patterns; ensure interoperability … grasp of responsible AI, security and compliance, including model risk management, privacy, data residency, human oversight and auditability.* Hands on understanding of AI/MLOps practices and platforms, including model lifecycle, observability, cost control, CI/CD, feature stores and data integration.* Experience defining and governing enterprise standards and architectures ...

Job Description We are seeking a Machine Learning Engineer with a strong foundation in Machine Learning/Deep Learning to join a cross‐functional team. You’ll work closely with data scientists and engineers to ...

involves creating scalable and secure infrastructures aligned with regulatory standards. The ideal candidate will possess strong leadership skills in cloud-native AI platforms and MLOps strategies, ensuring operational excellence in a highly regulated environment. #J-18808-Ljbffr ...

This hybrid role involves leading the design of ML platforms and infrastructure, ensuring effective deployment and monitoring of models. The ideal candidate has extensive MLOps experience, strong software engineering skills, and familiarity with cloud platforms such as GCP. Join a diverse team at iProov, working at the intersection of technology ...

specific core subject areas, enable cross-functional collaboration with other departments at Apple, contribute to architecture discussions, code review and proposals. Responsibilities Driving MLOps/LLMOps excellence within the team: including evaluation pipelines, monitoring, observability, and deployment best practices Building and maintaining LLM evaluation pipelines to assess model quality, track … pair programming and architecture discussions with other members of the team Minimum Qualifications Strong programming skills (Python, Swift, Go or other language) Experience with MLOps/LLMOps toolkits and frameworks Comprehensive knowledge and hands-on experience with LLM evaluations A learning attitude to continuously improve self and team Preferred Qualifications ...

About the Company - Entrust Identity Verification: At Entrust, identity is no longer a compliance checkbox—it’s a real-time product problem shaped by machine learning, adversarial behavior, and high-stakes decisions. Every verification is ...

About the Team : You'll be joining the team leading Entrust's Identity portfolio, formerly known as Onfido (an AI- powered digital identity solution). Our technology helps businesses verify real identities using machine learning ...

proof of concept into scalable production systems Align machine learning initiatives with product, commercial, and operational goals Advise on architecture across LLMs, agentic systems, MLOps, data pipelines, and model deployment environments Support leadership teams with AI roadmap prioritisation, delivery oversight, and capability planning Improve collaboration between product, data, and engineering … 10+ years of experience across machine learning, AI, or data-driven environments Experience leading ML teams within production-scale businesses Strong understanding of LLMs, MLOps, AI platform architecture, and scalable ML systems Experience operating within high-growth SaaS, AI-native, TechBio, or data platform businesses Ability to connect technical ...

We’re searching for a Head of Analytics & Data Science to transform how analytics is produced and consumed across our business. You’ll have significant freedom to unite, shape and lead our combined team, working ...

ML Ops Engineer Role Overview Were hiring an ML Ops Engineer to own the reliability, scalability, and operational integrity of our machine-learning systems in research & production. This role sits at the intersection of data ...