1 to 25 of 168 Remote/Hybrid MLOps Jobs in London

following would be highly advantageous: Large Language Models (LLMs) Amazon Bedrock Azure AI Foundry PyTorch FastAPI Knowledge Graphs Kubernetes Docker Infrastructure as Code MLOps Event-Driven Architecture Vector Databases Retrieval-Augmented Generation (RAG) Technology Stack Python SQL FastAPI PyTorch Docker Kubernetes AWS Azure Git CI/CD Knowledge Graphs ...

model development and deployment. Working knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation and evaluation methods. Experience with MLOps and engineering best practices, including CI/CD pipelines, model versioning, monitoring and automated testing. Experience deploying scalable AI solutions on cloud platforms such ...

.Experience building and deploying Generative AI solutions, including prompt engineering, fine-tuning, and evaluation of large language models (LLMs) or other generative architectures.Understanding of MLOps principles, including automated model training, CI/CD for ML, model versioning, monitoring, and retraining.Experience deploying AI and GenAI solutions on cloud platforms (AWS, Azure ...

compliant deployment of AI and Generative AI systems.Experience across one or more major cloud platforms (AWS, Azure, GCP) with Generative AI services.Grasp of MLOps/LLMOps principles, including CI/CD for ML, model monitoring, and governance frameworks.Proficiency with data processing and storage technologies (eg SQL, Spark)Strong stakeholder management ...

LLMs, Retrieval-Augmented Generation (RAG), AI agents, prompt engineering, and model evaluation.Experience building cloud-native applications on Azure, AWS, or Google Cloud.Strong understanding of MLOps, CI/CD, Kubernetes, containerisation, and scalable system architecture.Experience integrating AI into customer-facing products.Excellent communication skills with the ability to influence technical ...

incident management Experience with AI/ML frameworks such as PyTorch, TensorFlow, or JAX Exposure to LLMs, RAG, Agentic AI, model serving, or MLOps environments What's on Offer? Work on innovative AI and Agentic AI solutions Hybrid working from London 12-month initial contract Inside IR35 Opportunity to contribute ...

operational controls).Broad experience across major cloud platforms (AWS, Azure, GCP) and their AI/GenAI services; cloud-agnostic experience is preferred.Good grasp of MLOps/LLMOps practices (e.g., CI/CD, model/LLM monitoring, controls and auditability) and how to embed them pragmatically within client delivery.Familiarity with large ...

Generative AI systems.Broad experience across major cloud platforms (AWS, Azure, GCP) with Generative AI services. Cloud-agnostic experience is preferred.Strong grasp of MLOps/LLMOps principles, including CI/CD for ML, model monitoring, and governance frameworks.Proficiency with large-scale data processing and storage technologies (SQL, Spark, Hadoop ...

Intelligence, Data Science, or a related technology discipline Minimum 2:1 degree classification (or equivalent) Desirable Azure OpenAI, AWS Bedrock and Google Gemini experience MLOps, Data Engineering or Platform Engineering experience Experience leading technical teams Knowledge of Agentic AI, AI Agents and emerging AI frameworks What's On Offer? Opportunity ...

DevOps, and cloud-native architecturesPractical experience with Azure (Azure OpenAI, Azure AI Foundry, AI Search) and familiarity with AWS or GCPProficiency with containerized deployment, MLOps tooling (MLFlow, Airflow), and infrastructure-as-code practicesBachelor’s or Master’s degree in Computer Science, Data Science, or related fieldExperience with Azure AI Agent ...

scalability. Custom Code: Implement tailored machine learning code to meet specific needs. Data Engineering: Ensure efficient data flow between databases and backend systems. MLOps: Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage. ML Architecture Design: Create machine learning architectures using Google Cloud tools and services. Engineering ...

environments. Experience defining scalable AI architectures on cloud platforms such as Azure, AWS or GCP, leveraging cloud-native AI and data services. Understanding of MLOps and GenAIOps principles, including lifecycle management, monitoring, performance optimisation and operationalisation at scale. Exceptional stakeholder management, communication and leadership skills, with the ability to translate ...

scalability. Custom Code: Implement tailored machine learning code to meet specific needs. Data Engineering: Ensure efficient data flow between databases and backend systems. MLOps: Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage. ML Architecture Design: Create machine learning architectures using Google Cloud tools and services. Engineering ...

orchestration, tool use/function calling, RAG, agents, prompt engineering, or agentic patterns for enterprise workflows.Production ML/LLMOps: Experience with model deployment, monitoring, MLOps/LLMOps, and lifecycle management across cloud services (AWS Bedrock, Azure AI, GCP Vertex AI).AI Architecture & Security: Understanding of cloud reference architectures, identity ...

equivalent platforms.Developing high-quality, maintainable code and working with software engineering practices such as version control, testing, code review and CI/CD.Working with MLOps or workflow tools such as MLflow, Kubeflow, Argo or equivalent frameworks.Scaling models or analytical services for enterprise or operational use.Applying statistical methods, model evaluation techniques ...

concepts and applying them to business scenarios as neededDesirableExperience with/in:Microsoft Azure Data Scientist Associate (DP-100) certification or equivalent.Strong understanding of MLOps/DevOps concepts including model versioning, CI/CD, Automated retraining, lineage and governance.Experience with model risk management, Responsible AI and compliance controls in regulated ...

generative AI and the modern AI stack, including large language models, prompt engineering, retrieval-augmented generation (RAG), vector databases, evaluation/monitoring and MLOps principles, with the ability to shape investment cases and operating models at scale.Extensive experience providing executive oversight for production AI delivery (you steer architecture and engineering ...

e.g. LangChain/LangGraph)• Python and modern data engineering• Snowflake and governed semantic layers• AWS/GCP cloud services• Vector databases• Data pipelines and MLOps tooling• Business intelligence and visualisation tools#LI-AB3What’s in it For You Hybrid Work Model: We’ve adopted a flexible hybrid working environment ...

business teams to deliver end-to-end solutions.**Tech environment**Python, LLMs (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, MLOps/LLMOps, AWS, Azure, GCP, Databricks, Kubernetes and GPU infrastructure.**What we're looking for**Proven enterprise AI or ML architecture design, strong cloud ...

Build and maintain real-time AI pipelines using WebSockets and SSE, ensuring seamless low-latency delivery for voice (ASR/TTS) and text applications.Architecture & MLOps: Deploy and orchestrate models within containerized microservice architectures (Docker/Kubernetes), ensuring robust monitoring, security, and scalability.Collaborative Delivery: Work closely with Business Analysts and internal ...

maintain real-time AI pipelines using WebSockets and SSE, ensuring seamless low-latency delivery for voice (ASR/TTS) and text applications. Architecture & MLOps: Deploy and orchestrate models within containerized microservice architectures (Docker/Kubernetes), ensuring robust monitoring, security, and scalability. Collaborative Delivery: Work closely with Business Analysts and internal ...

maintain real-time AI pipelines using WebSockets and SSE, ensuring seamless low-latency delivery for voice (ASR/TTS) and text applications. Architecture & MLOps: Deploy and orchestrate models within containerized microservice architectures (Docker/Kubernetes), ensuring robust monitoring, security, and scalability. Collaborative Delivery: Work closely with Business Analysts and internal ...

maintain real-time AI pipelines using WebSockets and SSE , ensuring seamless low-latency delivery for voice (ASR/TTS) and text applications. Architecture & MLOps: Deploy and orchestrate models within containerized microservice architectures ( Docker/Kubernetes ), ensuring robust monitoring, security, and scalability. Collaborative Delivery: Work closely with Business Analysts and internal ...

technical stakeholders. Desirable Experience The following would be advantageous: Multimodal AI and reasoning. Edge or offline AI deployments. Kubernetes, particularly EKS or OpenShift. MLOps, including model evaluation, monitoring and reproducibility. Observability for agentic AI systems, including model performance, agent behaviour and drift. Agent orchestration and inter-agent communication protocols such ...

science tooling, including: Version control (Git) Cloud platforms (AWS, GCP, or Azure) Experiment tracking and model management Workflow orchestration and containerisation Strong understanding of MLOps principles, including model monitoring, retraining, and lifecycle management. Proven ability to translate business problems into data science solutions that deliver measurable outcomes. Beneficial Exposure ...