1 to 25 of 185 Permanent MLOps Jobs in the UK

frameworks, and modern AI workflows Experience with cloud platforms (AWS, GCP, or Azure) Understanding of distributed systems and large-scale data pipelines Experience with MLOps, CI/CD, and microservices architecture Familiarity with AI-assisted or agent-based development tools Strong stakeholder collaboration and communication skills Agile/Scrum experience ...

call/contact-centre analytics, Microsoft Teams Graph API, or telephony platforms (e.g. BroadWorks, Webex Calling). Knowledge of data governance, model ethics, and MLOps practices. Exposure to data visualization and predictive analytics for customer-experience metrics. Advanced degree in AI, ML, Data Science, or related discipline. Why Akixi ...

validation techniques to improve training pipelines Ensure data privacy and governance standards suitable for financial services Engineering best practices Contribute to DevOps and MLOps pipelines Implement robust version control, testing and CI/CD workflows Support reliable deployment and monitoring of models in production environments Technical leadership Mentor engineers ...

solutions in industry • Strong proficiency in Python and leading AI frameworks (e.g., scikit-learn, TensorFlow, PyTorch, LangChain) • Experience with data engineering, MLOps, CI/CD, and containerisation (Docker, Kubernetes) • Knowledge of cloud AI/ML platforms (AWS, Azure, or Google Cloud) • Strong understanding of ethical, secure, and responsible AI principles ...

Experience of working with client sponsors, both technical and non-technical, to collaboratively design requirements and build out solutions. Experience of designing and implementing MLOPs strategy and framework and proven track record in designing and delivering AI/ML solutions at scale, from concept to production. Deep understanding of Generative ...

Experience of working with client sponsors, both technical and non-technical, to collaboratively design requirements and build out solutions. Experience of designing and implementing MLOPs strategy and framework and proven track record in designing and delivering AI/ML solutions at scale, from concept to production. Deep understanding of Generative ...

solutions based on real user feedback and real-world constraints. Learn modern tooling and practices Gain hands-on experience with ML frameworks, cloud infrastructure, MLOps tools, and best practices for building scalable AI systems. Communicate clearly Translate technical findings into clear, structured insights for collaborators across technical and business teams. ...

future of AI, ML, and cloud innovation. Your next big opportunity starts here! For more details, visit : Website or LinkedIn Page . Role: MLOps Architect - AWS Experience Level: 8+ years Employment type: Full Time Location: Remote (UK) Description: We are seeking an experienced MLOps/AIOps Architect who can drive … implementation of MLOps strategy and also contribute broadly across other enterprise AI/ML programs. This role demands a strong architectural mindset, hands-on technical depth, and the ability to design scalable, cloud-native machine learning operations across traditional ML and modern LLM workflows. The ideal candidate will bring experience ...

scikit‐learn, TensorFlow/PyTorch), R, SQL, Jupyter notebooks and version control (Git). Machine Learning: regression, classification, clustering, tree‐based models, neural networks, MLOps practices and model deployment. Data Platforms: relational and NoSQL databases, time‐series stores, cloud data services (AWS Redshift, Azure Synapse, Google BigQuery) and distributed computing ...

Prefect. Data Quality and Lineage Familiarity with tools and techniques for data testing, documentation and lineage. Examples include Great Expectations or dbt. AI and MLOps Understanding of how to operationalise machine learning models in production, including model packaging, monitoring and controlled deployment. Containerisation and Kubernetes Confidence deploying applications in containerised ...

Prefect. Data Quality and Lineage Familiarity with tools and techniques for data testing, documentation and lineage. Examples include Great Expectations or dbt. AI and MLOps Understanding of how to operationalise machine learning models in production, including model packaging, monitoring and controlled deployment. Containerisation and Kubernetes Confidence deploying applications in containerised ...

Prefect. Data Quality and Lineage Familiarity with tools and techniques for data testing, documentation and lineage. Examples include Great Expectations or dbt. AI and MLOps Understanding of how to operationalise machine learning models in production, including model packaging, monitoring and controlled deployment. Containerisation and Kubernetes Confidence deploying applications in containerised ...

with: Python, SQL, Spark, Databricks Machine Learning, NLP, Deep Learning, and emerging AI (incl. GenAI/agentic AI) Cloud platforms (AWS, Azure, or GCP) MLOps/CI/CD, Terraform, Docker, Kubernetes Data visualisation tools like Power BI/Tableau What theyre looking for: 3+ years experience across data engineering ...

Vertex AI, Azure ML, Amazon SageMaker Experience of creating delivery client-facing proof of concepts Strong knowledge of cloud platforms (Azure, AWS, GCP) and MLOps best practices Experience with Generative AI and LLMs (e.g., RAG, fine-tuning, prompt engineering) Desirable Degree in Business, Computer Science, Engineering, or related field Personal ...

fast-paced environment Nice to Have Experience within financial services or other regulated industries Exposure to vector databases (e.g. Pinecone, Weaviate, FAISS) Experience with MLOps/LLMOps and deploying models into production Familiarity with LLM fine-tuning, evaluation, or guardrailing techniques Experience building internal tools such as copilots or knowledge ...

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 ...

libraries (e.g., spaCy, NumPy, SciPy, Transformers). Understanding of big data and cloud technologies (Spark, Hadoop, Azure, AWS, or GCP). Deep expertise in MLOps principles, scalable model deployment, and model governance. Excellent communication and stakeholder management skills with the ability to translate data insights into business value. Bonus Points ...

libraries (e.g., spaCy, NumPy, SciPy, Transformers). Understanding of big data and cloud technologies (Spark, Hadoop, Azure, AWS, or GCP). Deep expertise in MLOps principles, scalable model deployment, and model governance. Excellent communication and stakeholder management skills with the ability to translate data insights into business value. Bonus Points ...

/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 ...

driven initiatives, and a proven track record of delivering business impact. Key requirements are a deep technical understanding of data science, machine learning, and MLOps, alongside the ability to build and lead teams, communicate complex ideas to stakeholders, and align data strategy with broader business goals. You will play ...

PySpark. Agentic & Generative AI platforms such as Microsoft Copilot Studio, Adept AI, UiPath, OpenAI GPT-5 Agents, Orby AI, and Beam AI. DevOps and MLOps principles for production AI deployments. WHAT YOU'LL LOVE ABOUT WORKING HERE Data Science Consulting brings an inventive quantitative approach to our clients’ biggest business ...

PySpark. Agentic & Generative AI platforms such as Microsoft Copilot Studio, Adept AI, UiPath, OpenAI GPT-5 Agents, Orby AI, and Beam AI. DevOps and MLOps principles for production AI deployments. WHAT YOU'LL LOVE ABOUT WORKING HERE Data Science Consulting brings an inventive quantitative approach to our clients’ biggest business ...

Owning ML models and pipelines that extract structured knowledge from complex, unstructured documents at scale. ▸ NLP · Document AI · Data Pipelines · Evals 3. Staff SE – MLOps/DevOps for AI Shaping the infrastructure behind model serving, training pipelines, LLMOps, and AgentOps across the full AI stack. ▸ MLFlow · Databricks · Datadog · Terraform ────────────────────────── What ...

Owning ML models and pipelines that extract structured knowledge from complex, unstructured documents at scale. ▸ NLP · Document AI · Data Pipelines · Evals 3. Staff SE – MLOps/DevOps for AI Shaping the infrastructure behind model serving, training pipelines, LLMOps, and AgentOps across the full AI stack. ▸ MLFlow · Databricks · Datadog · Terraform ────────────────────────── What ...

systems Experience working with cloud platforms (AWS, GCP, or Azure) Understanding of distributed systems and scalable architectures Experience with CI/CD, microservices, and MLOps practices Ability to mentor and support engineers Strong collaboration and communication skills Experience working in Agile environments Degree in a relevant technical field (or equivalent ...