1 to 25 of 197 MLOps Jobs in London

Snowflake, and cloud platforms Implement batch and real-time data processing with structured and unstructured data Deploy solutions on cloud with CI/CD, MLOps tools, and ensure production reliability Collaborate with business teams, ensure compliance, and mentor teams on best practices Your Skills: 7+ years in data engineering ...

tools and web-based products that support forecasting, optimisation, and automated insight. Our projects span machine learning, statistical modelling, data engineering, data visualisation and MLOps, always with a focus on delivering tangible business outcomes. As demand for digital and AI-enabled transformation continues to grow, our Data Science team ...

Jira, Confluence, etc.) Desirable Experience Experience in Banking (essential focus area) Exposure to Model Context Protocol (MCP) Experience in regulated enterprise environments Understanding of MLOps/LLMOps practices Ability to contribute to ROI modelling, business cases, and AI value articulation Experience mentoring or leading delivery teams (Manager level) Role Focus ...

deploying production-grade AI systems using Azure, Databricks and Generative AI tooling Owning the implementation of ML pipelines, APIs and data integration workflows Enforcing MLOps best practices, coding standards, version control and performance optimisation Collaborating cross-functionally with data scientists, product managers, platform engineers, QA and audit professionals Mentoring junior ...

platform capability and implementation strategy Environment Python LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise AI or ML architectures Strong understanding of cloud and modern data platforms ...

Experience working with major cloud platforms such as AWS, Azure, or GCP. Familiarity with containerisation and orchestration technologies (e.g. Docker, Kubernetes). Exposure to MLOps practices or deploying AI/ML models into production environments. Experience working in agile or fast-paced delivery teams . ...

CrewAI . Data Architecture: Hands-on experience with Vector Databases (e.g., Pinecone, Milvus, Weaviate) and SQL/NoSQL systems. Infrastructure: Proven track record in MLOps , containerization (Docker/K8s), and cloud platforms (Azure/AWS). Ethical AI: A deep understanding of AI fairness, transparency, and regulatory compliance within ...

associated ML/DS libraries (scikit-learn, numpy, LightlGBM, Pandas, LangChain/LangGraph, TensorFlow, etc..) PySpark AWS cloud infrastructure: EMR, ECS, Athena, etc. MLOps: Terraform, Docker, Airflow, MLFlow More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans ...

Python, SQL LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases, FastAPI, APIs AWS, Azure, GCP or Databricks CI/CD, MLOps and LLMOps Requirements Strong software or data engineering foundations Experience delivering AI or AI-enabled solutions Exposure to cloud and modern data platforms Financial Services ...

systems Understanding of APIs, distributed systems, microservices, and system design Experience with cloud platforms such as Azure, AWS, or GCP Familiarity with DevOps/MLOps tooling including Docker, Kubernetes, CI/CD, monitoring, and logging Comfortable operating in fast-paced, product-led environments Nice to Have Experience with RAG pipelines ...

ability to work in fast-paced environments. Excellent communication and stakeholder management skills. Preferred Qualifications: Experience with machine learning data pipelines and MLOps practices. Knowledge of data streaming technologies such as Kafka or Kinesis. Familiarity with Terraform or similar infrastructure automation tools. Previous experience working in consulting or client-facing ...

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. #J ...

internal stakeholders.** *Excellent coding practice using Python and associated ML packages (HuggingFace, TensorFlow, PyTorch), following best practices.** *Experience developing robust ML pipelines with MLOps tools for model training, monitoring and deployment.** *Experience of appropriate version control and environment management (such as venvs or Docker).** *Experience of writing technical project ...

maintain ML solutions on modern frameworks to meet functional business requirements, adhering to software engineering best practices and with exposure to version control, testing, MLOps, CI/CD and API design Hands on experience in using one of 3 major cloud technologies (AWS, Azure or GCP) in a production environment ...

operationalize enterprise‐scale GenAI and agent‐based solutions. Experience with modern AI architectures including Retrieval‐Augmented Generation, agent design, and model orchestration. Familiarity with MLOps/LLMOps frameworks for governance, monitoring, and optimization. Leadership & Communication Exceptional ability to translate complex technical concepts into clear, actionable insights for executive audiences. Strong ...

London | AI Consultancy 📍 Central London (Hybrid - up to 2 days in office) 💰 £110,000-£150,000 + Benefits Skills/Tech: Machine Learning Lifecycle & MLOps | Python & Software Engineering Best Practices | Cloud Platforms (AWS, Azure, GCP) | Architecture & Security | Containers & Orchestration (Docker, Kubernetes) | ML/Statistical Methods | Deep Learning Frameworks (TensorFlow, PyTorch ...

Azure) and containerisation (Docker, Kubernetes). Solid understanding of ML fundamentals (model training, evaluation, common architectures). Desired Skills (Bonus Points) Experience with MLOps tooling (MLflow, Kubeflow, Weights & Biases, or similar). Building data pipelines (real‐time or batch) using tools like Apache Spark, Kafka, Airflow, or dbt. Familiarity with ...

operational efficiency. You will be expected to operate across the full ML lifecycle, from modelling and experimentation through deployment, monitoring and continuous improvement. Strong MLOps discipline is essential, including CI/CD, automated testing, repeatable deployments, versioning, and safe release practices to keep models stable, observable and maintainable in production. ...

such as GraphDB, Stardog, Neo4j, Amazon Neptune, or Blazegraph. Experience with embeddings, vector databases, RAG, LLM-based enrichment, or knowledge graph completion. Experience with MLOps tools such as Docker, Kubernetes, MLflow, Weights & Biases, Airflow, or similar. Experience with AWS, GCP, or Azure. Experience in construction, architecture, BIM, building materials, technical ...

every model we build is fair, free from bias, and explainable to consumers, customers, and regulators. Engineering & Deployment: Work within the engineering team on MLOps to containerise, deploy, and monitor models in high-scale production. Skills & Requirements Core Data Science: Foundational Stats: You must have an excellent grasp of Linear ...

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. Unleash ...

model artifacts, and inference outputs. Maintain audit trails for model decisions, data access, and deployment history. Attributes Required Qualifications 4+ years of experience in MLOps, ML Engineering, DevOps, or a closely related infrastructure role. Strong proficiency in Python for ML pipeline development, tooling, and automation. Hands-on experience with ...

monitoring, logging and observability platforms • Strong troubleshooting and communication skills • Experience working with Kubernetes, CI/CD pipelines or distributed systems • Exposure to AI, MLOps or LLM-based systems would be beneficial Nice to have: • Experience with RAG pipelines or orchestration frameworks • Exposure to Databricks, MLflow or AI observability tooling ...

Unix environments, Git, and testing practices Understanding of data governance and security best practices Desirable Skills Experience using GPUs in cloud environments Knowledge of MLOps, CI/CD pipelines, and monitoring ML models Experience building APIs and working with modern DevOps practices Eligibility Requirements Due to the nature ...

EXPERIENCE Strong experience delivering data science or machine learning solutions in production environments. Proven capability deploying and scaling models using appropriate engineering and MLOps practices. Advanced Python and SQL skills, with experience working in cloud environments such as AWS, GCP or Azure. Familiarity with modern tooling such as Databricks, version ...