1 to 25 of 97 Remote/Hybrid MLOps Jobs in England

Implement scalable, resilient architectures suitable for production use in a regulated financial services environment.* Integrate AI services into existing business applications and workflows.**Data, MLOps & Engineering*** Work with Data Engineers and Analysts to prepare, embed, and manage data for AI use cases, including vector databases and embeddings.* Implement CI/ ...

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 ...

frameworks (eg LangChain/LlamaIndex-style tooling, multi-agent patterns) - Prompt engineering and systematic evaluation/guardrail tooling (hallucination detection, safety testing) - LLMOps/MLOps - model versioning, deployment, monitoring, and rollback for AI services in production - Cloud-native engineering across major providers (AWS, Azure, or GCP), including their AI/ ...

similar data processing technologies. Experience deploying and operating AI systems in cloud environments using containerisation, managed ML services or serverless architectures. Understanding of MLOps practices including model versioning, experiment tracking, CI/CD for models and monitoring of model performance and drift. Experience applying responsible AI principles, including data privacy ...

required) Experience with AI frameworks such as LangChain, LangGraph or similar Experience deploying AI workloads within AWS, Azure or GCP Knowledge of MLOps, containerisation or Kubernetes Experience working on National Security, Defence or Intelligence programmes Familiarity with secure software development within highly classified environments ...

members and providing technical leadership. Desirable Skills & Experience MSc or PhD with a strong research background. Experience developing and maintaining robust ML pipelines and MLOps practices. Familiarity with version control, containerisation and environment management tools, including Git and Docker. Experience working within Linux environments and using command-line tooling. Experience ...

internal stakeholders. Excellent coding practice using Python and associated ML packages (HuggingFace, TensorFlow, PyTorch), following best practices. Experience developing robust ML pipelines with MLOps tools for model training, monitoring and deployment. Experience of appropriate version control and environment management (such as venvs or Docker). Experience of writing technical project ...

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 ...

practices. Hands‐on experience with Distributed Training infrastructure (Multi‐node GPU training, Kubernetes, vLLM). Familiarity with Deep Learning frameworks (PyTorch). Experience with MLOps tools and experiment tracking (e.g., ClearML, MLFlow, Weights & Biases). Research Fluency: Ability to read technical research papers and translate them into engineering requirements. ...

following attributes: Experience working with large-scale transactional, behavioural, or operational datasets Experience with cloud platforms such as Azure, AWS, or GCP Knowledge of MLOps, model monitoring, and deployment frameworks Exposure to Generative AI, Large Language Models (LLMs), or modern AI workflows Strong commercial awareness and the ability to translate ...

PyTorch, or TensorFlow Strong Python skills, with the ability to write clean, modular, production-grade code, and a solid understanding of data engineering and MLOps principles Ability to lead the end-to-end lifecycle of ML initiatives, work independently in ambiguous problem spaces, and mentor and grow other scientists ...

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 ...

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 ...

problem‐solver at heart. Nice to Haves (but we can teach you): You've already worked with Rust in a production environment. Experience with MLOps, particularly the challenges of managing the lifecycle of models in a multi‐tenant, high‐availability system. Familiarity with building ML inference services, model serialization (e.g. ...

track record of developing and deploying models at scale Expertise in Python, ML frameworks (TensorFlow, PyTorch), and experience with cloud platforms Strong familiarity with MLOps principles, CI/CD pipelines, and containerization (Docker, Kubernetes) With preferred backgrounds covering some of the following: Experience with speech-to-text or text ...

track record of developing and deploying models at scale Expertise in Python, ML frameworks (TensorFlow, PyTorch), and experience with cloud platforms Strong familiarity with MLOps principles, CI/CD pipelines, and containerization (Docker, Kubernetes) With preferred backgrounds covering some of the following: Experience with speech‐to‐text or text ...

experience in one or more object-oriented languages (e.g. Python, Go, Java, C++). Advanced SQL knowledge. Experience with modern ML engineering practices including MLOps, model lifecycle management, CI/CD, and monitoring. Knowledge of experimental design, analysis, and scientific methodology. Customer-centric and pragmatic mentality with a focus ...

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 ...

LangChain/LlamaIndex-style tooling, multi-agent patterns) LLM evaluation, guardrails, and red-teaming tooling (hallucination detection, safety/bias testing) LLMOps/MLOps: model versioning, deployment pipelines, monitoring, and rollback for AI services in production Fine-tuning and parameter-efficient adaptation techniques where appropriate to use case Data engineering ...

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 ...

level AI capability inside a large-scale AI adoption programme for a central government department. Where the Machine Learning Engineer trains models and the MLOps Engineer runs the platform, you sit closest to the end product: turning LLMs, retrieval systems, and agentic workflows into working, usable features that integrate with ...

management of SHE and cGMP. Desirable Skills/Experience PhD or equivalent experience in a relevant scientific, engineering, or data field. Demonstrated experience operationalising MLOps, model lifecycle management, and validation in GxP contexts. Familiarity with generative AI, knowledge graphs, semantic layers, and digital twins for manufacturing and CMC. Experience leading ...

management of SHE and cGMP. Desirable Skills/Experience PhD or equivalent experience in a relevant scientific, engineering, or data field. Demonstrated experience operationalizing MLOps, model lifecycle management, and validation in GxP contexts. Familiarity with generative AI, knowledge graphs, semantic layers, and digital twins for manufacturing and CMC. Experience leading ...

field Experience across a spectrum of libraries, frameworks and services : such as ML libraries e.g. TensorFlow or PyTorch model servers such as AWS Bedrock MLOps such as ML Flow or Sagemaker agent/AI orchestration such as LangGraph or MCP libraries Strong programming skills, especially in Python (other languages like ...

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 ...