101 to 125 of 537 MLOps Jobs in London

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. ...

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 ...

facing digital products, ideally in banking, fintech, or other regulated industries. Exposure to AI/ML platforms, model-serving environments, GPU-enabled infrastructure, or MLOps practices. Familiarity with security, compliance, and governance expectations in enterprise software environments. Experience contributing to platform modernization, developer experience initiatives, or multi-product engineering environments. ...

equivalent work experience. Preferred Skills: Experience with Azure, AWS, GCP, OpenAI, Anthropic, Mistral, Google/Gemini, Databricks, Snowflake, vector databases, orchestration frameworks, and MLOps/LLMOps tooling.Experience supporting large pursuits, proposal development, pricing reviews, or delivery mobilization.AI/ML, cloud, data platform, architecture, or security certifications.Strong analytical thinking, commercial judgment ...

least oneDeep experience with distributed computing with Apache Spark and knowledge of Spark runtime internalsFamiliarity with CI/CD for production deploymentsWorking knowledge of MLOps, ML/AI models and AI APIsDesign and deployment of performant production end-to-end data architectures and applications that combine data pipelines, ML/ ...

such as PyTorch or TensorFlow. Industry experience in researching or applying machine learning, especially if in recommender systems, ranking or personalisation Good understanding of MLOps and infrastructure concepts: CI/CD for ML, feature stores, model serving, observability, and versioning. Familiarity with containerisation and cloud‐native environments (e.g. Docker, Kubernetes ...

technical design and architecture for data‐science‐enabled applications, ensuring solutions are maintainable, supportable, observable and easy to operate. Productionise delivery with DevOps and MLOps, automating deployment in AWS and putting evaluation, monitoring and provider‐routing controls in place so AI outputs remain reliable and cost‐effective. Scale practical ...

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 ...

framework such as PyTorch or TensorFlow.Industry experience in researching or applying machine learning, especially if in recommender systems, ranking or personalisationGood understanding of MLOps and infrastructure concepts: CI/CD for ML, feature stores, model serving, observability, and versioning.Familiarity with containerisation and cloud-native environments (e.g. Docker, Kubernetes, GCP).Familiarity ...

production environments.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 (testing, code review ...

aspects of generative AI, collaborating with digital, platform, and governance stakeholders.Deploy and Scale AI SolutionsDrive model deployment activities, including containerization, testing, performance monitoring, and MLOps processes.Help utilise cloud platforms (AWS, Azure, GCP) for scalable data science and generative AI deployments.Work with engineers to support the deployment of machine‐learning algorithms ...

support the following: Digital sovereignty Compute optimisation Cost optimisation This role will touch on many elements, including data platforms, AI architecture, cloud architecture, MLOPs, integration frameworks, DevOps processes. You will work closely with the BA and Lead Consultant to make strategic recommendations. With the As-Is architecture you will work ...

decision-making and customer experience, applying patterns such as RAG, orchestration and multi-agent workflows, and tool/function calling.Establish best practices for LLMOps, MLOps and AIOps, including evaluation, observability, model routing, cost and latency optimisation, and secure deployment.Lead the development of reusable AI accelerators, patterns and reference architectures ...

Deep expertise in LLMs, RAG architectures, Prompt Engineering and Generative AI frameworks such as LangChain or LangGraph Hands-on experience with Python, PyTorch, TensorFlow, MLOps/LLMOps and cloud platforms (Azure, AWS or GCP) Experience deploying AI solutions within government, defence, healthcare or other high-assurance environments Strong understanding ...

engineering foundations, including version control, automated testing, and monitoring; Nice to have: Experience with agentic AI systems, GenAI and LLM systems in production, MCP, MLOps pipelines, real-time or streaming systems. Условия Competitive salary and company bonus scheme; Group pension scheme; Group healthcare and life assurance scheme; Hybrid working environment ...

Nice to haveExperience in one or more of our domains (assessment/psychometrics, workforce skills/ontologies, recommendations, fraud detection).Familiarity with MLOps practices (CI/CD for ML, experiment tracking, data/version control) in a cloud environment.How we work at PearsonPurpose‐driven, learner‐first; we prize curiosity, decency ...

experience with distributed computing with Apache SparkTM and knowledge of Spark runtime internals Familiarity with CI/CD for production deployments Working knowledge of MLOps, ML/AI models and AI APIs Design and deployment of performant production end-to-end data architectures and applications that combine data pipelines ...

applied AI Desirable Experience Hands-on experience with AWS services such as Lambda, ECS, S3, SageMaker or Bedrock Knowledge of ML engineering and MLOps practices (e.g. Docker, MLflow) Experience working in government, defence or other highly regulated environments Proven application of large language models (LLMs) to real-world problems Security ...

databases including Pinecone, Weaviate, Chroma, FAISS or Milvus.Experience building Retrieval Augmented Generation (RAG) solutions.Understanding of AI governance, Responsible AI and AI security principles.Exposure to MLOps, model deployment and monitoring frameworks.Experience working within a regulated industry such as Financial Services, Professional Services, Insurance or Consulting.What We're Looking ForA naturally curious ...

customer conversations — discovery, whiteboarding, architecture reviewsFamiliarity with one or more: data engineering (ETL/ELT, medallion architecture, streaming), data science/ML (model training, MLOps), or SQL analyticsStrong presentation and demo skills — you will build and present a live solution during the interviewBachelor's or Master's degree in Computer ...

Bedrock, SageMaker, OpenAI, Cognitive Services). Strong Python programming skills with hands‐on experience using frameworks like Autogen and LangGraph . Solid grounding in MLOps , containerisation (Docker, Kubernetes), and vector databases. Understanding of agent monitoring tools (Langfuse, OpenTelemetry). Strong software engineering best practices (testing, CI/CD, code reviews ...

machine learning principles, deep learning architectures, and natural language processing, particularly with large language models. Experience with cloud platforms (e.g., AWS, GCP, Azure) and MLOps tools for managing the AI lifecycle. Demonstrated ability to write clean, efficient, and well-tested code for scalable software systems. Exceptional problem-solving skills ...

embed these controls into enterprise architectures. Expertise in at least one major cloud platform (AWS or Azure), together with strong software engineering, security architecture, MLOps, and LLMOps knowledge. Next steps Computershare are dedicated to providing you with the opportunity to succeed on your own merits, starting from the application process. ...

policy considerations in applied AIDesirable ExperienceHands-on experience with AWS services such as Lambda, ECS, S3, SageMaker or BedrockKnowledge of ML engineering and MLOps practices (e.g. Docker, MLflow)Experience working in government, defence or other highly regulated environmentsProven application of large language models (LLMs) to real-world problemsSecurity ClearanceThis role ...