76 to 100 of 537 MLOps Jobs in London

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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. ...

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 ...

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 ...