51 to 75 of 409 MLOps Jobs in London

infrastructure Transform innovative data science prototypes into robust, scalable, and secure production software, taking ownership of the 'path to production' Drive the adoption of MLOps best practices (CI/CD for ML, model versioning, feature stores) to accelerate the feedback loop for Data Scientists Effectively communicate the complexities ...

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. #J-18808-Ljbffr ...

Data Mesh or Lakehouse architectures. Familiarity with Kubernetes, Docker, and real-time streaming technologies (e.g. Kafka, Kinesis). Exposure to ML engineering pipelines or MLOps frameworks. The Zego ways of working Teams work better with time to collaborate and space to get things done. We call it Zego Hybrid: some ...

Data Mesh or Lakehouse architectures. Familiarity with Kubernetes, Docker, and real-time streaming technologies (e.g. Kafka, Kinesis). Exposure to ML engineering pipelines or MLOps frameworks. Pay Transparency At Zego, we are committed to pay transparency and equal pay practices. In compliance with EU pay transparency regulations, we proactively disclose ...

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 ...

scalable enterprise workloads, integrating with clients' incumbent technology estates. Implement hybrid‐cloud and multi‐cloud strategies using modern data infrastructure (Kafka, Spark, data lakes), MLOps practices, and deployment platforms. Ensure compliance with data privacy regulations (including UK GDPR) and responsible AI standards, incorporating governance, observability, and security controls into ...

experience with RAG architectures, Agentic AI frameworks, and orchestration tools such as LangChain, LangGraph, AutoGen, and CrewAI. Proven experience applying Agile delivery, DevOps, MLOps, and LLMOps practices to enterprise AI programmes. Strong cloud architecture experience across Azure, AWS, or GCP. Experience in model deployment, monitoring, observability, governance, versioning, and operational ...

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 ...

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 ...

data pipelines and ML systems based on established patterns. Contribute to platform capabilities that support data transformation (DBT), pipeline orchestration (Airflow/Astronomer), MLOps workflows including model versioning and deployment automation and much more Support platform users including data engineers, ML engineers, data scientists, and analysts by troubleshooting issues, providing ...

expertise on AWS, especially Amazon Bedrock and Bedrock AgentCore, alongside SageMaker, Lambda and Step Functions, with CDK or equivalent IaC. Hands-on LLMOps and MLOps experience, including agent and trajectory evaluation, tracing (e.g. OpenTelemetry), guardrails, cost monitoring and automated model pipelines. A clear understanding of agent security risks, such ...

frameworks. Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Databricks, or equivalent cloud platforms. Data visualisation and storytelling tools such as Power BI. MLOps and model lifecycle management. Preferred Qualifications Experience in client-facing consulting roles. Exposure to cloud-based AI and analytics platforms. Understanding of Responsible AI and governance ...

domains. Ability to lead architectural discussions and technical trade‐offs without losing sight of the details. Familiarity with cloud infrastructure (AWS preferred) and modern MLOps practices. Commitment to people development: mentoring, performance conversations, and building high‐performing teams. Bonus: experience with NLP on financial contracts, RAG or LLM‐based applications ...

services such as Azure OpenAI, AI Foundry or AI Search, with Docker and Kubernetes Understanding of schema validation frameworks (e.g., JSON Schema, Pydantic) and MLOps tools such as MLflow or Airflow Strong communication ability to explain trade-offs around cost, latency and accuracy to technical and non-technical audiences Nice ...

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 ...

design principles. Technical Stack Core: Power BI SQL Server Tool-agnostic architecture patterns Cloud platform comparison (Azure, AWS, GCP), API design Microsoft Fabric MLOps frameworks What You'll Bring Professional experience spanning multiple areas of data and AI (analytics, engineering, or data science). Strong understanding of the complete data ...

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 ...

knowledge in the following: Python AWS Kubernetes Spark & distributed computing dbt Terraform Data Warehousing (we use Databricks but experience with other platforms is fine) MLOps implementation (desirable but not required) Airflow (or other orchestration tooling) Governance Things to note Just to let you know, we are dog friendly company ...

along with data extraction, manipulation and feature engineering techniques Experience with Spark, agile delivery methods and CI/CD practices Familiarity with DevOps and MLOps tools and practices, such as Docker, Terraform and MLFlow Confidence influencing and communicating with stakeholders across technical and non-technical teams Ideally, exposure to cloud ...

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 ...

production ML or data engineering experience considered. 8+ years of hands‐on experience building, deploying, and operating production ML systems on Azure, with practical MLOps and CI/CD experience including experiment tracking, deployment hygiene, monitoring, evaluation, and operational documentation. Strong Python and software engineering fundamentals, including testing, version control ...

Azure) and their data services. Tool & Product Development: Hands-on experience in developing and maintaining analytical tools, dashboards, and automated solutions. Familiarity with MLOps principles and practices for productising data science models. AI & Generative AI: Proven ability to identify and implement AI-driven solutions to enhance analytical capabilities and efficiency. ...

preparation, feature engineering, and feature stores, and understand why reproducibility and point-in-time correctness matter more than they first appear Have worked with MLOps in practice rather than in principle: model versioning and registries, experiment tracking, automated retraining, and monitoring for drift and model degradation (MLflow, SageMaker, Databricks ...

/IEC 42001, andNIST AI RMF. Experience working closely with cybersecurity, legal, and risk teams to ensure compliance and audit readiness. Strong background in MLOps and DevSecOps, including CI/CD pipelines, model monitoring, drift detection, and infrastructure‐as‐code. Skills : Ability to lead complex AI engineering programmes, set technical ...

PyTorch, or TensorFlow Strong Python skills, with the ability to write clean, modular, production-grade code, and a solid understanding of data engineering and MLOps principles Ability to lead the end-to-end lifecycle of ML initiatives, work independently in ambiguous problem spaces, and mentor and grow other scientists ...