151 to 175 of 819 MLOps Jobs in the UK

modern generative models or agentic development approaches can enhance data processing and analytics solutions.Contribute to the adoption of advanced analytics and AI capabilities, including MLOps and AIOps.Data Management, Security & GovernanceEnsure data security and compliance with relevant standards and regulations, particularly in public sector and regulated environments.Implement robust data management ...

machine learning, and generative AI technologies to deliver innovative business solutions.Continuously improve platform performance, scalability, and operational efficiency while supporting AI governance and MLOps best practices.Skills we would love you to have Experience working with Snowflake, including SQL development, data modelling, orchestration, and integration management.Experience with Azure services, including Azure ...

Multimodal Pipelines - Prior exposure to conversational voice pipelines or multimodal integrations via hyperscaler services. Advanced AI/ML Frameworks - Exposure to frameworks (TensorFlow, PyTorch), MLOps practices, and cloud AI platforms like Google Vertex AI or AWS Sagemaker. Education - BS in Computer Science, Engineering, Data Science, or equivalent technical field ...

anomaly detection.Experience with Generative AI, LLMs, RAG architectures or agent-based AI solutions.SQL and large-scale data analysis.Cloud-based data science platforms and MLOps practices.Data visualisation and storytelling tools.Desirable SkillsExperience within financial services, regulated environments or market infrastructure.Familiarity with Azure AI services and Microsoft AI ecosystem.Experience with observability ...

their AI/ML services.Proficiency in data manipulation and analysis using SQL and big data technologies (e.g., Spark, Hadoop) is a plus.Experience with MLOps tools and best practices for model deployment, monitoring, and governance.Education and Experience:Bachelor's or Master's, or equivalent degree in Computer Science, Data Science, Statistics ...

habit of fixing the class of problem rather than the instance. AI and ML operations: Experience operating model serving, LLM inference or MLOps pipelines in production is a strong plus. Familiarity with model APIs, vector databases, MCP or orchestration frameworks helps. Networking and security: VPC design, service mesh and zero ...

habit of fixing the class of problem rather than the instance. AI and ML operations: Experience operating model serving, LLM inference or MLOps pipelines in production is a strong plus. Familiarity with model APIs, vector databases, MCP or orchestration frameworks helps. Networking and security: VPC design, service mesh and zero ...

habit of fixing the class of problem rather than the instance. AI and ML operations: Experience operating model serving, LLM inference or MLOps pipelines in production is a strong plus. Familiarity with model APIs, vector databases, MCP or orchestration frameworks helps. Networking and security: VPC design, service mesh and zero ...

habit of fixing the class of problem rather than the instance. AI and ML operations: Experience operating model serving, LLM inference or MLOps pipelines in production is a strong plus. Familiarity with model APIs, vector databases, MCP or orchestration frameworks helps. Networking and security: VPC design, service mesh and zero ...

Generative AI.Agent frameworks and orchestration technologies.Retrieval Augmented Generation (RAG) architectures.API development and microservice design.Cloud-native development (Azure preferred).CI/CD automation and MLOps practices.Vector databases and knowledge retrieval platforms.Data modelling and distributed systems design.AI observability, evaluation and governance frameworks.Desirable ExperienceExperience within Financial Services or highly regulated environments.Knowledge of Service ...

orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool‐use patterns, human‐in‐the‐loop workflows, and agentic safety at enterprise scale. AI/ML Engineering & MLOps: Full AI/ML lifecycle ownership: training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using platforms such ...

orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool-use patterns, human-in-the-loop workflows, and agentic safety at enterprise scale* AI/ML Engineering & MLOps: Full AI/ML lifecycle ownership: training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using platforms such ...

OpenAI, Google Vertex AI, Amazon Bedrock, Microsoft Azure AI, Anthropic, or similar ecosystems.* Familiarity with vector databases, knowledge management systems, AI orchestration frameworks, and MLOps practices.* Experience with data architecture, analytics, and enterprise integration patterns.* Cloud certifications (AWS, Azure, GCP) and AI-related certifications.* Experience leading AI transformation initiatives ...

orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool-use patterns, human-in-the-loop workflows, and agentic safety at enterprise scaleAI/ML Engineering & MLOps: Full AI/ML lifecycle ownership: training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using platforms such ...

modern software development 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. ...

DevOps and business teams to deliver end-to-end solutions.Tech environmentPython, 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 forProven enterprise AI or ML architecture design, strong cloud and modern data ...

models into Azure enterprise environments. Azure Databricks, Azure Machine Learning and ideally Azure AI Foundry. Building production AI applications using LangChain, LangGraph and Python. MLOps/LLMOps including deployment, monitoring, governance and lifecycle management. RAG, vector databases, prompt engineering and tool‐calling patterns. Building end-to-end AI/ ...

coursework.Communication & Collaboration: Strong verbal and written communication skills, with the ability to work effectively in a team environment and learn from senior members.Desirable experienceCloud & MLOps Exposure: Basic familiarity with a cloud platform (AWS, GCP, or Azure) and an awareness of concepts like containerization (Docker).Core ML Knowledge: A good grasp ...

machine learning fundamentals, statistics, and working with complex datasets to support model development, training, fine‐tuning, and inference. Experience with CI/CD, containerisation, MLOps/LLMOps workflows, and monitoring AI models in production, including evaluating performance, safety, and cost efficiency. Ability to translate complex business challenges into effective ...

GenAI Depth: Proven experience architecting solutions with frontier modelsincluding prompt engineering, fine-tuning, RAG, and agentic/multi-agent system design. LLMOps/MLOps Grounding: Expertise in evaluation frameworks, observability, cost and latency optimization, guardrails, and safe deployment at scale. Cloud and Model Ecosystems: Direct experience with major model providers ...

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 ...

machine learning fundamentals, statistics, and working with complex datasets to support model development, training, fine-tuning, and inference. Experience with CI/CD, containerisation, MLOps/LLMOps workflows, and monitoring AI models in production, including evaluating performance, safety, and cost efficiency. Ability to translate complex business challenges into effective ...

Skills: Hands-on experience with data lineage tools and techniques, including graph & vector databases and metadata management platforms Hands-on experience with LLM Ops, MLOps, and AI/ML platform deployment at scale Familiarity with business-led analytics delivery models and rapid prototyping frameworks Experience with AI/ML governance ...

Specialist Team). The impact you will have Lead the architectural design of production‐grade ML workloads on our unified platform, encompassing the entire MLOps lifecycle from end‐to‐end pipeline creation and optimization (training/inference) to seamless integration with cloud‐native services. Provide advanced technical support ...

projects and deployments of AI systems. Provide guidance and mentoring to other team members on best practices in AI engineering. Use best practices (e.g., MLOps, AIOps) to improve products/services and processes related to AI. Optimise existing model serving and data pipelines to meet changing performance and security requirements. ...