126 to 150 of 819 MLOps Jobs in the UK

Science, Machine Learning, Operational Research, or Statistics. Academic, internship, or personal projects delivering data science outputs. Basic familiarity with CI/CD pipelines or MLOps concepts. Exposure to containerisation technologies (e.g., Docker). Some experience working with relational or NoSQL databases (e.g., PostgreSQL, MongoDB). Familiarity with Workday data structures ...

identity and access management with OIDC/SAML and Azure Entra IDExperience with LLMs, RAG systems, prompt engineering and AI evaluation frameworks; familiarity with MLOps, model deployment and AI observability/guardrailsStrong familiarity with AI-assisted development tools (e.g. Claude Code)Department overviewThe AI Hub is responsible for enabling ...

machine learning solutions from initial experimentation through to deployment. Strong knowledge of data science libraries such as Polars, Pandas, NumPy, Scikit-learn. Knowledge of MLOps tooling such as Weights & Biases, AirFlow, AWS SageMaker or Docker. Experience working with cloud platforms such as AWS, Azure or GCP. Experience working collaboratively ...

cost/latency optimisation).Exposure to MCP (Model Context Protocol) and tool-based/function-calling architectures.Experience deploying and operating machine learning models/MLOps pipelines (e.g. model serving, monitoring, retraining workflows).Experience with real-time/streaming systems.What’s in it for youOur rapidly growing, award-winning business offers ...

Computer Science, Data Science, Machine Learning, or related field.Proven experience building and deploying AI applications in large-scale production environments.Experience with MLOps practices and tools for managing the machine learning lifecycle.Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn.Strong understanding of data preprocessing, feature engineering ...

Computer Science, Data Science, Machine Learning, or related field. Proven experience building and deploying AI applications in large-scale production environments. Experience with MLOps practices and tools for managing the machine learning lifecycle. Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn. Strong understanding ...

Computer Science, Data Science, Machine Learning, or related field. Proven experience building and deploying AI applications in large-scale production environments. Experience with MLOps practices and tools for managing the machine learning lifecycle. Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn. Strong understanding ...

other product and engineering teams to deploy AI safely, rapidly, and at scale. You will democratize AI across the organization, establish modern LLMOps/MLOps practices, and directly impact the Actian Data Intelligence Platform by introducing agentic workflows, intelligent data pipelines, and cutting-edge capabilities. Core Responsibilities: Internal AI tooling ...

across financial services, healthcare, manufacturing, retail, public sector, or other enterprise industries. Cloud & Infrastructure: Hands‐on experience with cloud architecture, Infrastructure‐as‐Code, DevOps, MLOps, or enterprise platform engineering. Pre‐sales & Customer Success: Experience supporting enterprise sales cycles, solution demonstrations, technical workshops, or proof‐of‐concept engagements. Enterprise Integrations: Experience ...

Engineering (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 to the Solution ...

maintain real-time AI pipelines using WebSockets and SSE , ensuring seamless low-latency delivery for voice (ASR/TTS) and text applications. Architecture & MLOps: Deploy and orchestrate models within containerized microservice architectures ( Docker/Kubernetes ), ensuring robust monitoring, security, and scalability. Collaborative Delivery: Work closely with Business Analysts and internal ...

Deep knowledge of LLM APIs, prompt engineering, and conversational AI patterns. Experience in fine‐tuning LLMs for domain‐specific enterprise applications. Strong expertise in MLOps and LLMOps, ensuring scalable, reliable, and monitorable model deployments. Experience with agentic frameworks and autonomous agent architectures. Proficiency in Python and modern software development practices ...

incrementality testing; comfort with both Bayesian and frequentist approaches. Cloud and analytics platforms (Azure, AWS, GCP, Databricks, Microsoft Fabric, Snowflake) and exposure to MLflow, MLOps, or Spark/PySpark. Exposure to Generative AI or AI-assisted analytical workflows. Perks on Perks: Competitive salary and hybrid work model – come hang ...

Develop and implement machine learning algorithms, models, and computational systems; design and build scalable data pipelines to support model training and production with DevOps & MLOps Customize and apply Deep Learningand Gen AI models for various use cases based on the business needs, data availability,systemand infrastructure requirements - including edge device ...

technical stakeholders. Desirable Experience The following would be advantageous: Multimodal AI and reasoning. Edge or offline AI deployments. Kubernetes, particularly EKS or OpenShift. MLOps, including model evaluation, monitoring and reproducibility. Observability for agentic AI systems, including model performance, agent behaviour and drift. Agent orchestration and inter-agent communication protocols such ...

with measurable impact, including deploying or working alongside ML/AI systems in production Bonus: Trust & Safety, Content Moderation, or Integrity engineering experience MLOps experience: deployment, monitoring, and versioning of ML models Experience with SQL, data analysis tools, real-time streaming systems (Kafka, Redis), or event-driven architectures Familiarity with ...

Skills:Hands-on experience with data lineage tools and techniques, including graph & vector databases and metadata management platformsHands-on experience with LLM Ops, MLOps, and AI/ML platform deployment at scaleFamiliarity with business-led analytics delivery models and rapid prototyping frameworksExperience with AI/ML governance, prompt engineering ...

Generative AI.Demonstrated ability to architect end-to-end Data & AI solutions, with specific expertise in modern Generative AI concepts (e.g., fine-tuning, RAG, MLOps for LLMs), using either open source and/or ISV tools such as Dataiku, Domino, DataRobot etc.Strong proficiency in a core programming language (e.g., Python ...

data science solutions. Ensure sharing best practices and learnings with other squad members. Design and spearhead the development of Gen AI, AI Agents and MLOps platforms, applications, and solutions driven by GenAI, deep learning and machine learning to address business challenges and elevate product performance. Build standards and recommendations ...

datasets, and digging into why something failed.You've invested deliberately in your software engineering craft: testing, error handling, CI/CD, observability, and the MLOps practices that keep things healthy once they're live.You're hands-on across the GenAI stack: LLM APIs, retrieval and vector stores, agents with tool ...

digging into why something failed. You've invested deliberately in your software engineering craft: testing, error handling, CI/CD, observability, and the MLOps practices that keep things healthy once they're live. You're hands-on across the GenAI stack: LLM APIs, retrieval and vector stores, agents with tool ...

technical stakeholders. Desirable Experience The following would be advantageous: Multimodal AI and reasoning. Edge or offline AI deployments. Kubernetes, particularly EKS or OpenShift. MLOps, including model evaluation, monitoring and reproducibility. Observability for agentic AI systems, including model performance, agent behaviour and drift. Agent orchestration and inter-agent communication protocols such ...

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 ...

comfortable documenting and defending architectural decisions and engaging in thoughtful technical debate.Not required, but it’s a plus if you also have:· Experience with MLOps/LLMOps frameworks and best practices· A PhD in Computer Science, Electrical Engineering or a related field.· A background or work experience in life-sciences ...

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. Unfortunately we are unable to progress with candidates that require sponsorship at this time Skills we would love you to have Experience working ...