51 to 75 of 90 MLOps Jobs in London

learning. Developers should be familiar with libraries like NumPy, pandas, scikit-learn, TensorFlow, and PyTorch. Familiarity with best practice in Machine Learning workflows and MLOps tools. Fluency in validation and evaluation framework and metrics frameworks for machine learning such as Accuracy, Recall, F1, Intersection over Union, and others. Additional beneficial ...

MLOps Engineer Location: London, UK (Hybrid - 2 days per week in office) Day Rate: Market rate (Inside IR35 Duration: 6 months Role Overview As an MLOps Engineer, you will support machine learning products from inception, working across the full data ecosystem. This includes developing application-specific data pipelines, building ...

experimental design Experience developing APIs and embedding models within production systems Hands-on experience with cloud-based AI infrastructure and modern LLMOps/MLOps workflows Demonstrated ability to balance speed of delivery with technical excellence Ability to collaborate effectively Ability to communicate effectively If you are interested in this ...

Operations, Digital, Risk). Knowledge of AI governance, risk, compliance, and responsible AI frameworks relevant to FS environments. Exposure to AI delivery teams, including MLOps, data engineering, AI consulting, or agent-development teams. Ability to translate complex technical capability into clear commercial value propositions. Comfortable assessing AI maturity, operating models ...

GDPR/DPA 2018); hands-on with platforms such as Microsoft Purview, Azure ML, Databricks, Power BI, Collibra, or Alation; exposure to MLOps practices. ...

frameworks (e.g., LangGraph, PydanticAI) and managing memory and tool-calling in production. Engineering Mindset : Ability to write clean, testable Python and familiarity with modern MLOps stacks. Product Experience : Experience shipping ML systems in production for real users, with an experimental mindset and high bar for AI ethics. Why Join Wilgot ...

innovation and continuous learning. Guide the selection and implementation of AI technologies, platforms, and tools. Ensure best practices in model development, deployment, and MLOps while cultivating deep partnerships with SaaS providers, in-house engineering teams and product management teams across the UK business. Collaborate with UK-wide business units ...

deliver AI and analytics solutions aligned with the organisations Data & AI strategy. Key Responsibilities * End-to-end development of AI/ML solutions. * MLOps practices: CI/CD, model monitoring, retraining. * Use of open-source and enterprise tools (LangChain, Azure OpenAI, Databricks). * Generative AI features: embeddings, RAG, AI agents. …/ML delivery. * Legal/professional services experience is a plus. * AI/ML frameworks: PyTorch, TensorFlow, LangChain. * Cloud: Azure (preferred), AWS, GCP. * MLOps: CI/CD, model lifecycle, monitoring. * Generative AI: LLMs, RAG, chat agents. * Data engineering alignment: ETL, governance. * Strong coding, communication, and collaboration skills. * Strategic thinking, problem ...

from the Ultralytics export toolkit . Knowledge of cloud AI services and training infrastructure across AWS, Google Cloud, and Azure, including GPU clusters and MLOps considerations. Proven ability to lead benchmarking, technical validation, and co-development programs using our benchmark capabilities and integration best practices. Strong ability to connect technical ...

production deployment — while mentoring top engineers and collaborating closely with research and infrastructure leaders. You'll combine technical depth in deep learning and MLOps with leadership in execution and strategy, ensuring that Turing's AI initiatives deliver reliable, high-performance systems that translate research breakthroughs into measurable business impact. This … navigating the intersection of research, engineering, and product delivery. Roles & Responsibilities: Lead and mentor a cross-functional team of ML engineers, data scientists, and MLOps professionals. Oversee the full lifecycle of LLM and ML projects — from data collection to training, evaluation, and deployment. Collaborate with Research, Product, and Infrastructure teams ...

experimental models into production-ready solutions, collaborating with engineers on deployment, monitoring, and optimisation. Build and maintain ML pipelines using AWS services and modern MLOps/LLMOps tooling. Implement robust versioning, experiment tracking, and reproducibility practices. Mentor junior engineers and contribute to team-wide AI knowledge sharing. Communicate complex technical … level). Hands-on experience with Python ML frameworks (scikit-learn, PyTorch, TensorFlow, XGBoost). Experience with AWS ML services (SageMaker, Lambda, S3) and MLOps/LLMOps tooling (MLflow, Weights & Biases, DVC). Proven track record transitioning models from experimentation to production with governance and quality controls. Experience developing ...

Senior ML Engineer will own the journey from experimentation and hypothesis testing through to secure, production-grade deployment, using a modern AWS-based MLOps and LLMOps platform. The Role The successful candidate will balance rapid experimentation with production readiness, prototyping and validating machine learning and generative AI approaches while ensuring …/B testing and statistical evaluation Proven experience moving models from experimentation into production with appropriate governance and quality controls Hands-on experience with MLOps tooling such as MLflow, Weights & Biases or Data Version Control Practical experience building LLM/GenAI applications, including prompt engineering and retrieval-augmented generation ...

want to work with the latest in LLMs, co-pilots, agentic workflows, RAGs , and more, while also applying real data science, ML, and MLOps skills in live enterprise environments. What You’ll Do: Work directly with enterprise clients to design and deploy custom AI agents Tackle complex business problems with ...

simulation + tele‐op logs, version them, apply weak‐supervision labelling, curate balanced datasets, and auto‐surface fresh failure cases into retraining. Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‐time edge inference. We’re Looking For: 3+ years building deep‐learning systems ...

Details Role/Job Title: MLOps Engineer Work Location: London, UK Office Requirement (Hybrid): 2 days per week Key Responsibilities (High-Level) Design, implement, and maintain scalable ML model deployment pipelines (CI/CD for ML). Build infrastructure to monitor model performance, data drift, and other key metrics … production. Develop and maintain tools for model versioning, reproducibility, and experiment tracking. The Role As an MLOps Engineer , you will support these products from inception. This requires working across the full data ecosystem: developing application-specific data pipelines (features), building CICD pipelines that automate the training and deployment of machine ...

lifecycle, ensuring engineering solutions meet operational needs. Implementing and maintaining secure, scalable architectures in collaboration with cloud, platform, and security teams. Applying DevOps and MLOps practices to support reliable, auditable, and maintainable deployments. Contributing to technical design reviews, estimates, and solution documentation. Supporting proposals and technical inputs to bids where … production ML workflows. Exposure to Generative AI, LLMs, or agent-based systems, including orchestration and integration into wider platforms. Understanding of DevOps/MLOps practices, CI/CD, monitoring, and model governance. Defence & Secure Environment Experience Experience delivering technical solutions within MOD, Defence suppliers, Defence consultancies, or other secure public ...

Role - Senior Lead Analyst- Data Scientist Technology – Data science, MLops, GenAI Location – London, UK Job Description Today, the corporate landscape is dynamic, and the world ahead is full of possibilities! None of the amazing things we do at Infosys would be possible without an equally amazing culture, the environment where … leadership roles with team management. •Strong expertise in Azure Machine Learning, Python, and ML frameworks (TensorFlow, PyTorch, Scikit-learn). •Hands-on experience with MLOps, CI/CD pipelines, and model monitoring. •Knowledge of cloud architecture, data engineering, and security best practices. •Design and implement ML pipelines on Azure Machine ...

does for its clients. Often involves the following engineering disciplines : Cloud Engineering Data Engineering (not building pipelines but designing and building the framework) DevOps MLOps/LLMOps Often work with the following technologies : Azure, AWS, GCP Airflow, dbt, Databricks, Snowflake, etc. GitHub, Azure DevOps and related developer tooling …/CD platforms, Terraform or other Infra-as-Code MLflow, AzureML or similar for MLOps; LangSmith, Langfuse and similar for LLMOps The difference to our "AI Engineer" role is: Do you "use/consume" these technologies, or are you the one that "provides" them to the rest of the organization. ...

retail. Our mission is to change retail forever through hyper-personalised experiences that are both simple and beautiful. About the Role As a Senior MLOps Engineer, you will own the infrastructure and tooling that turns experimental models into dependable production systems. You will build the pipelines, monitoring, and deployment workflows ...

create real commercial leverage Skills and Experience Proven leadership in defining and delivering enterprise AI strategy, including GenAI Strong understanding of AI platforms, MLOps, and modern AI tooling (without needing to be hands-on) Experience translating AI opportunities into commercially impactful use cases at scale Exceptional communication, influence, and stakeholder ...

teams Build and maintain end-to-end ML pipelines (training, validation, inference, monitoring) Ensure models meet requirements around performance, resilience, and explainability Contribute to MLOps best practices, model governance, and technical standards Support model monitoring, drift detection, and ongoing optimisation Required Experience Proven commercial experience as a Machine Learning Engineer … with cloud platforms (AWS, GCP, or Azure) Nice to Have Experience with fraud, risk, credit, AML, pricing, or customer analytics use cases Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, etc.) Docker and Kubernetes experience Exposure to model governance, explainability, or regulatory frameworks Contract Details £650–£750 per day (Outside IR35 ...

Collaborate with product and engineering to integrate ML systems into user-facing features Develop and deploy production-quality machine learning frameworks, while maintaining robust MLOps pipelines Stay up-to-date with the latest advancements in AI, conduct research, and explore innovative techniques Influence key decisions on architecture and implementation … including latency, token cost control, context window constraints, monitoring and prompt versioning Strong Python skills and experience with PyTorch, TensorFlow or Keras Exposure to MLOps practices and hands-on experience with Sagemaker and/or Vertex AI Bachelor's degree in a technical field such as computer science or years ...

Collaborate with product and engineering to integrate ML systems into user-facing features Develop and deploy production-quality machine learning frameworks, while maintaining robust MLOps pipelines Stay up-to-date with the latest advancements in AI, conduct research, and explore innovative techniques Influence key decisions on architecture and implementation … including latency, token cost control, context window constraints, monitoring and prompt versioning Strong Python skills and experience with PyTorch, TensorFlow or Keras Exposure to MLOps practices and hands-on experience with Sagemaker and/or Vertex AI Bachelor's degree in a technical field such as computer science or years ...

Science, or NLP/LLM. Strong Python skills; exposure to PyTorch/TensorFlow/Hugging Face. (Bonus) understand fundamentals of deep learning, LLMs, and MLOps, vector databases, embeddings, or retrieval-augmented generation (RAG). Loves solving complex problems and thrives in a fast-moving startup environment. Is curious, ambitious … Processing, Large Language Models, Generative AI, GenAI, Neural Networks, PyTorch, TensorFlow, Hugging Face, OpenAI, LangChain, RAG, Retrieval-Augmented Generation, Python, Data Science, AI Research, MLOps, Data Pipelines, Prompt Engineering, Model Fine-Tuning, Cloud Computing, AWS, Azure, Google Cloud, AI Infrastructure, Transformers, Reinforcement Learning, Vector Databases, Pinecone, Weaviate, Semantic Search ...

Train customers and community members on product capabilities Feed frontline insights back to the company to shape the product roadmap Stay ahead of DevOps & MLOps trends and bring that expertise to every customer conversation IND123 ...