51 to 75 of 226 MLOps Jobs in London

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 ...

prioritizing and executing tasks to meet timelines and deliver results. What you’ll bring 5+ years as an ML-focused software engineer, ML Engineer, MLOps Engineer, or similar, with hands-on production experience. Proven expertise with ML model deployment, API design, and integration into production environments. Strong Python programming ...

container orchestration (Kubernetes), service mesh, CI/CD, IaC, observability, and platform engineering Strong knowledge of real‐time streaming (Kafka/Pulsar), batch analytics, MLOps, model governance, and responsible AI patterns Security and resiliency expertise: zero trust, secrets management, identity, encryption, chaos engineering, disaster recovery, and site reliability practices Integration ...

with stakeholders and subject matter experts. Familiarity with high compute cloud‐based platforms and services, in particular AWS. Familiarity with code version control and MLOps deployment approaches. Required/Preferred Education and Licenses Advanced degree (MS or PhD) in Data Science, Computer Science, Biostatistics, or related field. 3 – 5 years ...

underwriting Financial data/loan documentation Deal scoring/probability of default Exposure to agentic workflows/LLM orchestration Experience with production ML or MLOps Details Contract: 6 months rolling Rate: £750 (Outside IR35) Location: Fully remote Start: ASAP ...

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 ...

scalable applications. Experience making architecture and technology decisions within enterprise organisations. Strong knowledge of modern AI engineering practices, including LLMs, RAG, agentic workflows, and MLOps concepts. Experience working with cloud platforms, ideally AWS. Ability to balance innovation with risk, governance, and operational requirements. Strong stakeholder management and communication skills. Experience ...

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 ...

Willingness to work on‐site in our Alkmaar office Nice to Have (But Not Required) Experience deploying ML models into production environments Familiarity with MLOps tooling such as MLflow, CI/CD workflows, and model monitoring Experience with AWS, Azure, or GCP Understanding of real‐time systems, motion synthesis ...

solutions with clarity, assurance and accountability at scale. To be successful as an AI Ops Platform Engineer, you should have experience with: LLMOps/MLOps at production scale, operating the full Generative AI lifecycle including models, prompts and agents, CI/CD pipelines, structured evaluation, drift and hallucination monitoring ...

communication skills Experience contributing to and overseeing large-scale rollouts and infrastructure initiatives. Expertise in several of the following areas: robust python development, MLOps, containerization, cloud infrastructure (AWS and Azure), advanced search and recommendation technology, orchestration of ML pipelines for real-time inference, microservices architecture, model monitoring and governance. What ...

Databricks is seeking a Senior Specialist Solutions Architect for AI/ML in London. You will be the trusted technical expert for ML/AI, guiding customers and the Field Engineering team to design production ...

Engineer and ML Engineer through to Senior, Lead, Applied Scientist and Research Engineer positions—for upcoming roles across applied machine learning, generative AI, MLOps and model productionisation. These are hands-on engineering roles where you'll build, train, ship and operate the models that drive real product behaviour and real … models at scale LLM and generative AI tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows) MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent) Cloud experience (AWS, GCP, Azure), containerisation and basic data engineering/SQL Familiarity with experimentation ...

into enterprise platforms Collaborating closely with business stakeholders to translate requirements into scalable AI solutions Driving best practice across AI governance, model monitoring and MLOps Evaluating emerging AI technologies and identifying opportunities for innovation Skills & Experience We're looking for candidates with experience across several of the following: Commercial experience … learn Experience with Generative AI technologies including LLMs, LangChain or LlamaIndex Hands-on experience building RAG solutions and working with vector databases Experience with MLOps, CI/CD and containerisation Strong SQL skills and experience working with large datasets Cloud experience across Azure, AWS or GCP Excellent communication skills with ...

Experience: Strong commercial understanding of the modern AI ecosystem, including: Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Vector Databases MLOps Experience leading the implementation of enterprise-scale AI solutions and being accountable for delivery outcomes. Knowledge of cloud platforms such as AWS, Azure ...

platforms: Microsoft Azure (Fabric, Synapse, ADLS), AWS (Redshift, Glue, Lake Formation), GCP (BigQuery, Dataproc, Dataplex). Strong working knowledge of AI/ML engineering, MLOps, LLMOps, Generative AI, and Agentic AI frameworks. Familiarity with data governance tooling: Unity Catalog, Alation, Collibra, Purview, Snowflake governance, or equivalent. Ability to review, challenge ...

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 ...

delivered through POSIT/RStudio. Key deliverables: Design a consolidated analytics and data science platform on Databricks. Enable notebook-based development, data science experimentation, MLOps, API integrations, and application deployment. Support external data ingestion, scheduling, BI connectivity, developer tooling, and operational reporting. Enable AI and LLM integration patterns. Develop migration ...

impact. Experience with agentic telemetry, evaluation services, and orchestration of NLQ workflows. Demonstrated leadership working with engineers, data scientists, and AI practitioners. Familiarity with MLOps practices and AI pipelines. Hands‐on experience building and maintaining user interfaces for NLQ and data exploration. EEO Statement We recognize that our people ...

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 ...

modern AI technologies and concepts, including: Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Vector Databases AI Solution Architecture MLOps Principles AI Governance and Risk Considerations Candidates are not expected to be hands-on developers but should be capable of guiding technical teams and contributing ...

approaches Agent-based architectures A practical understanding of how to apply LLMs to real-world use cases Tools & Engineering Experience with model deployment and MLOps tools (e.g. Databricks, MLflow) Familiarity with version control (Git) and collaborative development practices Awareness of best practices in code quality and reproducibility Communication & Consulting Ability ...

generation, environment design, mid-training, supervised fine-tuning, offline reinforcement learning, online reinforcement learning, reward modelling, transition modelling, etc. Our team also has dedicated MLOps, Infra and Inference support at scale. We focus on improving the long horizon, goal-conditioned instruction-following of large models for GUI/Computer ...

delivers.* You enjoy working collaboratively with Product Owners, Engineers, Analysts and business stakeholders to solve meaningful problems.* Experience with cloud-based Machine Learning platforms, MLOps, LLMs or Agentic AI is advantageous, although we're more interested in your ability to learn quickly and apply new technologies to solve business problems. ...

organisations solve complex operational, technical and regulatory challenges through AI adoption at scale. You will work across multidisciplinary teams, collaborating with data scientists, architects, MLOps/LLMOps engineers, business stakeholders and senior leadership to design, deliver and scale AI products, agentic AI solutions and data driven applications. Key responsibilities: Building … Using Python and SQL to build robust, scalable AI and data solutions Working with cloud platforms such as AWS, Azure, GCP or Databricks Supporting MLOps, LLMOps, CI/CD and software engineering best practice Collaborating with technical and non-technical stakeholders across complex programmes Helping identify technical, delivery, security, data ...