151 to 175 of 224 MLOps Jobs in London

cases, coordinating their lifecycle from ideation through to optimisation and, where appropriate, decommissioning. Ensure solutions are production‐ready and scalable through the adoption of MLOps, AI guardrails, and emerging capabilities, maintaining long‐term sustainability. Governance, Risk & Regulatory Compliance Define and integrate a comprehensive AI governance, risk, and control framework aligned ...

data platforms for analytics and AI/ML Deep Microsoft Azure expertise Hands‐on experience with Databricks, APIs, and integration platforms Strong understanding of MLOps and productionizing machine learning Experience leading and scaling engineering teams Tech stack: Azure, Databricks, Python, PySpark, SQL, JavaScript, Node, Bash THE ROLE … systems that support analytics, ML workloads, and real‐time operations Create API‐first integration layers for internal teams and external partners Implement MLOps best practices for model deployment and lifecycle management Lead exceptional teams Build, mentor, and scale high‐performing engineering teams Set standards for code quality, security, and performance ...

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 ...

production environment — not just notebooks. You've owned models after they ship and know how to keep them healthy. Full‐Lifecycle Delivery (MLOps): Comfortable delivering the end‐to‐end production lifecycle — model training, versioning, monitoring, and champion/challenger experimentation — without relying on a dedicated ML engineering team to carry … what to do about it. [Bonus] Experience with VertexAI TOOLS & TECHNOLOGIES Languages: Python, SQL Data & Transformation: dbt, Snowflake, BigQuery Visualisation & BI: Looker Engineering & MLOps: Docker, GitHub Workflow & Orchestration: Vertex AI Pipelines (GCP), Kubeflow LLMs & GenAI: Gemini API, Claude API INTERVIEW PROCESS Step 1: People Team Screening Call (30 min) Step ...

/ML models, from development to deployment, monitoring and optimisation. You will lead the design of scalable, production‐grade ML systems and establish robust MLOps practices in a governance‐driven environment. You will also drive platform architecture, ensure safety and compliance, and mentor a high‐calibre ML engineering team ...

involves architecting cloud-native systems and leading engineering teams to deliver high-quality solutions using Azure and Databricks. Candidates should possess deep knowledge of MLOps strategies and have hands-on experience in building integrations. This hybrid position offers the opportunity to drive innovation and business impact while fostering a culture ...

ability to drive adoption, influence behaviours, and deliver outcomes at pace Exposure to technologies, modern cloud platforms, customer data platforms, enterprise BI tooling, or MLOps governance environments would be highly advantageous. Why Join? This is an opportunity to define and shape governance strategy during a significant period of enterprise transformation. ...

strongly preferred. Experience writing about complex technical topics for external audiences is required. Experience with AI, GPUs, Kubernetes, networking, datacenter infrastructure, cloud platforms, MLOps, sovereign AI, or enterprise infrastructure is a major plus. Why should you join us? What sets us apart is our blend of modern technology, competitive benefits ...

infrastructure changes. Azure DevOps Pipelines : Design, build, and maintain secure, high‐speed CI/CD pipelines within Azure DevOps to automate code promotion and MLOps workflows. Incident Management : Lead the response, containment, and resolution of critical platform outages, serving as a primary escalation point for high‐priority cloud and infrastructure … requirements, post‐mortems, and engineering strategies effectively to both technical and non‐technical stakeholders. AI/ML Platform Automation : Architect and maintain highly available MLOps platforms across AWS and Azure environments. Developer Enablement : Build self‐service infrastructure tools, deployment templates, and continuous training/inference pipelines to eliminate friction. ...

infrastructure changes. Azure DevOps Pipelines : Design, build, and maintain secure, high-speed CI/CD pipelines within Azure DevOps to automate code promotion and MLOps workflows. Incident Management : Lead the response, containment, and resolution of critical platform outages, serving as a primary escalation point for high-priority cloud and infrastructure … requirements, post-mortems, and engineering strategies effectively to both technical and non-technical stakeholders. AI/ML Platform Automation : Architect and maintain highly available MLOps platforms across AWS and Azure environments. Developer Enablement : Build self-service infrastructure tools, deployment templates, and continuous training/inference pipelines to eliminate friction. ...

optimizing the ML systems that power drug discovery processes, ensuring scalability across vast datasets. The ideal candidate will have hands-on experience in infrastructure, MLOps, and distributed systems, and be excited to work with ML technologies. The position is hybrid, requiring office presence at least 50% of the time. Compensation ...

that combine machine learning, privacy, and software engineering to transform how market research is conducted. This team sits at the intersection of platform engineering, MLOps, distributed systems, and Generative AI. Working alongside researchers, data scientists, and product teams, you'll help scale a platform that supports synthetic data generation, Digital … research. Key Responsibilities • Define and own the technical vision for a large-scale AI platform • Lead and grow a team of Software, Data and MLOps Engineers • Build scalable GCP, Kubernetes, and Vertex AI infrastructure • Establish engineering standards, SDLC processes, and delivery practices • Partner with researchers to productionise advanced ...

experimentation through to productised, maintainable solutions that deliver measurable value. Drive engineering excellence across ML systems, including CI/CD, testing, observability, reliability, and MLOps guidelines. Define technical standards, patterns, and protocols for ML engineering and applied ML science across teams. Lead complex, multi-team technical initiatives and influence organisational … systems, scalable architectures, and API design. Advanced programming experience in languages such as Python, Go, Java, or C++. Advanced SQL knowledge. Strong experience with MLOps, production ML systems, model lifecycle management, and monitoring. Experience working with large-scale data systems and distributed computing frameworks (e.g. Spark, Hadoop). Knowledge ...

Job Title: Lead AI/ML ConsultantJob Location: London, UK/HybridJob Type: FTEWe are seeking a highly experienced AI Consultant to design, implement, and drive enterprise-grade AI solutions on the Azure platform Microsoft ...

Senior AI Transformation Leader - London (Hybrid) - Up to £115,000 An established organisation delivering AI and digital transformation solutions is seeking a Senior AI Transformation Leader to support the growth of its Telecoms, Media & Technology ...

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 ...

will lead the VLA pretraining roadmap, define architecture choices, data strategies, and scaling while growing a team of research engineers. You will collaborate with MLOps and data platforms to optimize large-scale training, evaluation, and deployment for real-world robotics, shaping capability ceilings across our fleet. #J-18808-Ljbffr ...

engineers training foundation policies on diverse real-world and synthetic data. You will design large-scale distributed training, establish evaluation suites, and collaborate with MLOps and Data Platform teams to ensure smooth transfer to post-training and real-robot inference. #J-18808-Ljbffr ...

Role Summary We are seeking a Principal Machine Learning Engineer (SageMaker, MLOps, Model Governance & Explainability) to provide technical leadership across the full lifecycle of machine learning systems powering a new matching platform. This role is accountable for defining ML architecture, establishing engineering standards, driving MLOps maturity, and ensuring that … architecture for the matching platform, including data pipelines, model training workflows, inference runtimes, and telemetry ecosystems. Lead adoption of best‐in‐class MLOps patterns, platform tooling, and AWS SageMaker capabilities across training, processing, registry, monitoring, and deployment. Partner with platform, security, and data engineering teams to implement scalable data lakehouse ...

Intelligence Architect: Proven experience architecting solutions across Generative AI, Agentic AI, machine learning and automation Strong understanding of prompt engineering, RAG pipelines, model tuning, MLOps and LLMOps Experience delivering enterprise‐scale RAG solutions using LLMs and vector databases Ability to design AI platforms that integrate with existing enterprise technology stacks … services Solid software engineering foundation for building scalable, maintainable AI systems Advanced Python expertise with AI and ML frameworks Experience with MLOps and LLMOps tooling including containerisation and orchestration platforms Proven experience delivering applications using state‐of‐the‐art LLMs including prompt design, fine‐tuning and evaluation Strong understanding ...

ownership: Make decisions that move the whole company forward. We value autonomy, speed, and impact. Stack: Build with modern tools — Python, FastAPI, TypeScript, LLMs, MLOps What We’re Looking For 2+ years of software development experience Hands-on experience building production AI systems with Python, PyTorch, and LLMs (OpenAI, Anthropic … etc.), integrated into real user workflows Familiarity with MLOps tools and CI/CD for both traditional and ML-driven systems Strong backend and API development skills - comfortable owning systems end-to-end, from data pipelines to user-facing interfaces Passion for building clean, usable products that solve real problems ...

optimize Generative AI features, focusing on fine-tuning open-source LLMs (using LoRA, QLoRA, etc.) and building RAG pipelines. End-to-End Deployment (MLOps): Partner with our AI Engineers to transition your models from prototype into scalable, monitored, and reliable production microservices. Technical Mentorship: Actively contribute to code reviews, share … tuning open-source models. Production-grade Python: High proficiency in Python and modern software practices (TDD, clean code, code review, CI/CD). MLOps exposure: Experience with tools and frameworks for training, deploying, and monitoring models in production. DEIB Commitment We believe attracting and retaining the best talent ...

scenario studies, and clear articulation of trade‐offs. Lead development of ML/AI solutions end‐to‐end (feature engineering, model training/validation, MLOps, monitoring and drift management) with rigorous controls, documented governance, and demonstrable business value. Uphold standards for documentation, reproducibility, traceability, and SDLC; ensure compliance with internal … governance, and regulatory expectations for pricing/risk models. Hands‐on ML/AI for quant finance (signal extraction, surrogate modelling, anomaly detection), including MLOps, drift monitoring, and explainability. Knowledge of portfolio optimisation, hedging algorithms, execution analytics, and transaction cost modelling. Familiarity with distributed computing and market data tooling (e.g. ...

powering our enterprise planning platform. The role blends traditional predictive mathematics with Generative AI, requiring deep ML knowledge, GenAI experience, and strong Python/MLOps practices to deliver scalable, reliable solutions across our global customers. #J-18808-Ljbffr ...

machine learning projects on large event‐based datasets, ensuring models move from research to reliable production. Additionally, you will act as technical bridge to MLOps, embedding best‐practice deployment techniques and shaping the company‐wide AI and GenAI strategy. #J-18808-Ljbffr ...