1 to 25 of 32 MLOps Jobs in Central London

data quality. Stay current with emerging AI and machine learning technologies and identify opportunities for innovation. Your Profile Essential Skills & Experience Proven experience in MLOps, data engineering, software engineering, or machine learning development with recent exposure to Generative AI and advanced analytics. Strong understanding of software engineering best practices, including ...

solutions on GCP leveraging Vertex AI, BigQuery, Cloud Run, secure API integrations; build scalable, resilient, compliant architectures; integrate AI services into existing applications. Data, MLOps & Engineering: Work with Data Engineers and Analysts to prepare, embed, and manage data (vector databases, embeddings); implement CI/CD, monitoring, logging, model performance tracking ...

models into Azure enterprise environments. Azure Databricks, Azure Machine Learning and ideally Azure AI Foundry. Building production AI applications using LangChain, LangGraph and Python. MLOps/LLMOps including deployment, monitoring, governance and lifecycle management. RAG, vector databases, prompt engineering and tool-calling patterns. Building end-to-end AI/ ...

expertise preferred, with hands‐on exposure to AWS and GCP. Experience integrating AI into distributed systems, microservices, APIs, and event‐driven architectures. DevOps and MLOps experience including CI/CD, IaC, automated testing, observability, and deployment automation. Familiarity with data engineering, ML models, Responsible AI, and enterprise governance. Ability ...

concepts. Excellent programming skills in Python. Demonstrated experience in building, validating, deploying, monitoring, and updating production ML and AI models. Hands-on experience with MLOps and AIOps infrastructure and tooling. Proficient in problem-solving and analytical reasoning. Exceptional communication and collaboration skills. Experience with ML frameworks such as TensorFlow, PyTorch ...

software development concepts like CI/CD, Version Control, Containers (Docker & Kubernetes) and proven track record of deploying ML models to production and established MLOps concepts. A good command of analytical programming and visualisation languages and libraries (SQL, python, matplotlib, etc.) as well as some of the supporting tools ...

cloud platforms such as Microsoft Azure, Amazon Web Services AWS, and Google Google Cloud. Experience with Generative AI frameworks, AI orchestration platforms, and MLOps practices. Proven track record of delivering AI solutions from concept through production at enterprise scale. Understanding of Responsible AI, AI governance, model lifecycle management ...

have strong experience across: Generative AI Agentic AI Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Prompt Engineering Machine Learning Intelligent Automation MLOps and LLMOps You'll also have experience working with technologies such as OpenAI, Azure OpenAI, Hugging Face, Llama or similar Large Language Models, alongside vector databases including ...

associated architecture patterns Experience designing and managing APIs including REST and GraphQL Strong understanding of microservices and serverless architectures Practical experience with DevOps and MLOps practices including CI/CD pipelines Knowledge of security frameworks, identity management and authentication standards Desirable Skills for the AI Solutions Architect: Experience with enterprise ...

Mathematics Computer Science or a related quantitative discipline Desirable Skills for the Data Scientist: Experience with deep learning frameworks advanced modelling techniques Familiarity with MLOps concepts model monitoring deployment pipelines Exposure to tools such as Databricks MLflow feature stores Experience working in Agile product-led delivery environments Understanding of data ...

techniques to power systems analysis, curtailment modelling, constraint analysis and renewable energy optimisation. Prototype and deploy AI solutions using cloud technologies and modern MLOps practices where appropriate. Produce clear visualisations and technical insights for planning applications, investment decisions and stakeholder reporting. Continuously evaluate emerging AI technologies and identify opportunities ...

learning frameworks, and Generative AI/LLM components. Pipelines: Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, vector databases, and scalable data processing streams. MLOps: Implement model deployment, monitoring, testing, and lifecycle management using standard CI/CD and containerized workflows. Architecture: Contribute to technical design decisions, trade-off analyses … Azure, or GCP) using Docker and Kubernetes. Engineering: Strong software engineering fundamentals, including version control, testing, and API/microservices architecture. Experience with MLOps platforms (e.g., MLflow, SageMaker) or agentic AI frameworks. Familiarity with secure development practices or regulated/high-compliance environments. Background in NLP, computer vision, or data ...

deployment models across enterprise environments Advising clients on suitable AI use cases and opportunities for enterprise-wide adoption Collaborating with Enterprise Architects, Data Scientists, MLOps Engineers, DevOps teams and business stakeholders to shape and deliver solutions Selecting and implementing appropriate cloud, open-source and commercial AI technologies Leading the delivery … hosting Retrieval Augmented Generation (RAG), vector databases, semantic search and embedding techniques Python development and AI frameworks such as PyTorch, TensorFlow and LangChain MLOps frameworks and AI model lifecycle management API integration and enterprise-wide AI solution delivery Presenting architecture solutions to senior stakeholders, governance forums and architecture boards ...

engineering excellence across the team. Partner with sales and tech leadership to define requirements for high‐value opportunities, deploying specialized experts (Machine Learning Operations (MLOps), Generative Media (GenMedia), or Agentic systems) to key accounts. Lead technical hiring for FDE, evaluating AI/ML expertise, systems engineering, and coding skills ...

hybrid in London, moving AI from prototype to production across cloud-native platforms. Responsibilities include AI solution design, cloud engineering on GCP, data, MLOps, Responsible AI governance, and collaboration with underwriters and operations. Strong Python and LLM experience required. #J-18808-Ljbffr ...

enterprise-scale operating models and shared services environments is beneficial. Required Skills AI & Agent Platforms: Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry Data & MLOps: Databricks, Azure SQL, ADLS, Azure AI Search Cloud & Integration: Microsoft Azure, API Management, Containers Identity & Security: Azure Entra ID, Key Vault, RBAC Observability & Governance: Logging ...

SuperSet, Tableau or PowerBI is a bo nus)Hands-on experience in machine learning model development, evaluation, and production deployment, with familiarity in MLOps principles to build scalable and standardised workflows and implement effective ML monitoring sys temsProven ability to create polished presentations and effectively communicate insights to customers with ...

such as SuperSet, Tableau or PowerBI is a bonus) Hands-on experience in machine learning model development, evaluation, and production deployment, with familiarity in MLOps principles to build scalable and standardised workflows and implement effective ML monitoring systems Proven ability to create polished presentations and effectively communicate insights to customers ...

ensure solutions are secure, ethical, and fit for purpose. Working across multidisciplinary teams, you'll drive innovation in areas like DevOps, platform engineering, and MLOps - balancing experimentation with governance and risk. What you'll be doing: Leading architecture, design, and delivery of digital & AI solutions Driving innovation across AI, analytics ...

monitoring, supportability and reliability concerns in production - Experience using infrastructure-as-code tools such as Terraform - Confidence taking responsibility for live systems and outcomes MLOps experience is highly attractive, particularly operating AI or machine learning models in production. The focus is on operating models, not training them. In Return ...

experiences, and helping define the next generation customer data and marketing technology ecosystem. Equally important will be establishing how Data Science, AI Engineering and MLOps functions operate together in an AI-enabled business, whilst defining the long-term operating model for Consumer Data Science as the organisation continues its transformation. ...

solutions from development through to deployment and monitoring * Building scalable Generative AI and Agentic AI solutions, including RAG pipelines and orchestration frameworks * Implementing MLOps frameworks, governance and best practices * Translating business requirements into cloud-native AI and data architectures across Azure, AWS and GCP * Collaborating with technical and non-technical … stakeholders to deliver measurable business value * Supporting innovation initiatives, reusable assets, thought leadership and client proposals Candidates should have strong experience in AI Engineering, MLOps and cloud-native AI platforms, alongside hands-on knowledge of technologies such as LangChain, LlamaIndex, MLflow, Kubernetes, Docker and CI/CD tooling. Applicants will ...

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 ...

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. ...

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 ...