1 to 25 of 121 Remote/Hybrid MLOps Jobs in the UK

Snowflake, and cloud platforms Implement batch and real-time data processing with structured and unstructured data Deploy solutions on cloud with CI/CD, MLOps tools, and ensure production reliability Collaborate with business teams, ensure compliance, and mentor teams on best practices Your Skills: 7+ years in data engineering ...

Learning, Generative AI, or Large Language Models (LLMs) Knowledge of RAG architectures and vector databases Experience with Docker, Kubernetes, and containerised applications Exposure to MLOps and AI deployment pipelines Knowledge of graph databases and knowledge graph technologies Previous experience working within Defence, National Security, or highly regulated sectors Contract Details ...

deploying production-grade AI systems using Azure, Databricks and Generative AI tooling Owning the implementation of ML pipelines, APIs and data integration workflows Enforcing MLOps best practices, coding standards, version control and performance optimisation Collaborating cross-functionally with data scientists, product managers, platform engineers, QA and audit professionals Mentoring junior ...

varied stakeholders Desirable Experience Knowledge of Knowledge Graphs and Graph Analytics Experience working within Defence, National Security, Government, or highly regulated environments Exposure to MLOps and model deployment practices Experience with vector databases and semantic search technologies Understanding of AI governance, ethics, and security considerations What's on Offer Hybrid ...

systems Understanding of APIs, distributed systems, microservices, and system design Experience with cloud platforms such as Azure, AWS, or GCP Familiarity with DevOps/MLOps tooling including Docker, Kubernetes, CI/CD, monitoring, and logging Comfortable operating in fast-paced, product-led environments Nice to Have Experience with RAG pipelines ...

/CD and infrastructure as code. • Experience of optimising systems for performance and scalability and reliability. • Understanding of machine learning workflows or MLOps for deploying and monitoring models in production. • Experience with cloud platforms, especially AWS and its data services (e.g., BigQuery, Redshift, Athena). • Knowledge of network protocols ...

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 ...

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 ...

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 ...

operational efficiency. You will be expected to operate across the full ML lifecycle, from modelling and experimentation through deployment, monitoring and continuous improvement. Strong MLOps discipline is essential, including CI/CD, automated testing, repeatable deployments, versioning, and safe release practices to keep models stable, observable and maintainable in production. ...

every model we build is fair, free from bias, and explainable to consumers, customers, and regulators. Engineering & Deployment: Work within the engineering team on MLOps to containerise, deploy, and monitor models in high-scale production. Skills & Requirements Core Data Science: Foundational Stats: You must have an excellent grasp of Linear ...

Unix environments, Git, and testing practices Understanding of data governance and security best practices Desirable Skills Experience using GPUs in cloud environments Knowledge of MLOps, CI/CD pipelines, and monitoring ML models Experience building APIs and working with modern DevOps practices Eligibility Requirements Due to the nature ...

engineering initiatives Mentor and support junior engineers within the team Contribute to engineering standards, reusable patterns, and AI best practices Help shape scalable MLOps and AI deployment approaches Drive high-quality engineering practices across testing, deployment, monitoring, and optimisation Tech environment Key technologies include: Python Azure Databricks Generative AI/ ...

recommendation systems Familiarity with scientific, biomedical, or scholarly datasets Experience with knowledge graphs, ontologies, or semantic enrichment systems Exposure to production ML systems and MLOps practices Publications or applied research contributions in NLP, IR, search, or generative AI Experience building AI systems in regulated, high-trust, or content-rich domains ...

data engineering and contribute to architectural decisions. Support senior team members in identifying and addressing data science opportunities. Required Skills & Experience Proven experience in MLOps or DevOps roles within machine learning environments. Strong programming skills in Python, with hands‐on experience in PySpark and SQL. Deep understanding of ML lifecycle ...

PyTorch, or Hugging Face Solid understanding of machine learning fundamentals and performance evaluation techniques Experience working in cloud platforms (AWS, GCP, or Azure) with MLOps tools (e.g. MLflow, SageMaker, Vertex AI) Comfortable working independently and delivering high-quality work to tight timelines Experience working in fast-paced environments or scale ...

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 ...

GenAI solutions, from data preparation through to production deployment. Designing and building robust ML pipelines, CI/CD processes and MLOps frameworks. Leading GenAI initiatives such as document processing, LLM fine-tuning, prompt engineering and orchestration using modern frameworks. Organising and processing large, complex datasets to enable ...

personalization Forecasting and optimization Ensure models are scalable, explainable, and production-ready Work closely with engineering teams to operationalize models within production environments (MLOps) Product & Innovation Partner with Product, Engineering, and Architecture teams to embed AI capabilities into SBS solutions Identify opportunities to leverage AI/ML to enhance banking ...

Supporting the full ML lifecycle, from experimentation to deployment and optimisation.* Applying best practices in data engineering and contributing to architectural decisions.* Using modern MLOps tools and CI/CD approaches to improve reliability and efficiency.* Contributing to a culture of knowledge-sharing and continuous improvement.## ## Technical ExperienceWe ...

knowledge — spectral features, neural codecs, generative audio models Experience deploying models to cloud inference (AWS or GCP) with awareness of latency and cost tradeoffs MLOps competency — experiment tracking, model versioning, evaluation pipelines, ML CI Experience with Python and modern ML frameworks (PyTorch, JAX) Excellent verbal and written English communication skills ...

Learning (ML) Natural Language Processing (NLP) Generative AI (GenAI/LLMs) Translate business requirements into technical AI solutions that deliver measurable impact Model Deployment & MLOps Implement and manage end-to-end ML pipelines, from data ingestion to model deployment and monitoring Collaborate with DevOps and engineering teams to ensure scalable ...

curated data layers. Establish and enforce standards for coding, CI/CD, testing, and deployment across all data delivery teams. Ensure consistent implementation of MLOps & MLflow for model lifecycle management and reproducibility. Establish & Lead Data Solution Architecture practice to ensure design integrity and proper execution of data architecture patterns. Lead ...

commercial problems Develop, validate, deploy, and monitor machine learning models in production environments Build and maintain end-to-end data science solutions using modern MLOps practices Use cloud-based platforms (notably Databricks) to develop scalable data products Translate complex analytical outputs into clear, business-focused insights Collaborate with technical ...

Competencies Exposure to AI governance, security, or compliance considerations. Experience deploying ML models in cloud environments (Azure preferred, AWS acceptable). Familiarity with MLOps practices (model versioning, evaluation, monitoring, iteration). Experience working with non‐ideal, real‐world data rather than curated research datasets. Experience working in both enterprise ...