1 to 25 of 130 Remote/Hybrid MLOps Jobs in the UK

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 ...

optimize relational data models using PostgreSQL. Deploy and manage backend services on cloud platforms, preferably Azure and/or GCP. Collaborate with DevOps/MLOps engineers on CI/CD pipelines, containerization, and deployment strategies. Monitor service health and performance, proactively addressing bottlenecks and reliability issues. Work within an Agile ...

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 ...

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/ ...

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/ ...

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 ...

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 ...

Predictive modelling and machine learning Building production-ready AI applications Communicating technical concepts to both technical and non-technical stakeholders Experience with Microsoft Fabric, MLOps, compliance analytics or risk modelling would be a bonus, but isn't essential. Why This Role? This is an opportunity to join a business where ...

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. ...

analytics alongside AI solutions). Technical Scope Platform‐agnostic engineering mindset; able to work across varied client stacks. Experience with cloud AI services and MLOps/LLMOps (e.g., Azure AI Foundry, AWS SageMaker and/or Bedrock). Strong software engineering practices: Python, APIs, containerisation, CI/CD, IaC, testing ...

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 ...

curiosity to learn others. Bridging Data & AI: Understanding how AI fits into the broader data landscape, interest in integrating LLMs and AI/MLOps into client solutions. Collaborative Leadership: Proven track record of supporting multi-squad deliveries, guiding stakeholders through architectural trade-offs, and contributing to technical proposals. Our Benefits ...

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 ...

Computer Science, Machine Learning, or a related technical discipline. Bonus Skills/Experience Public writing, talks, or threads about building with AI. MLOps and model serving experience (BentoML, MLflow, Vertex AI, SageMaker). Streaming and batch ingestion pipelines (Spark, Airflow, Beam, Glue). Healthcare or life sciences domain exposure. ...