76 to 100 of 110 MLOps Jobs in the City of London

but Valuable) Worked at a Pre-Seed or Seed-stage startup through to Series A Built or deployed software used by enterprise customers Experience with applied machine learning or MLOps (not just academic or notebook-based work) Exposure to event-driven architectures, streaming systems (Kafka, etc.), or real-time data pipelines Frontend experience in React, TypeScript, or similar modern frameworks More ❯

including conferences, publications, and industry forums About You • Proven record in AI and machine learning strategy and delivery, ideally within the financial or professional services sector • Strong experience with MLOps and scaling AI applications into production environments • Deep understanding of systems architecture and engineering principles, with experience leading full stack technology teams • Commercial awareness of how AI can drive growth More ❯

Engineers (Junior to Mid-Level) who 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 intelligent, scalable solutions Blend More ❯

and delivering intelligent solutions that solve real business problems at scale. What you’ll bring: experience with traditional data science and machine learning (solid stats, programming, ideally exposure to MLOps, etc.) critical: Hands-on experience building production-grade solutions using LLMs, RAGs, MCPs, and agentic workflows. Client-facing experience with a forward-deployed engineering mindset. You’ll work directly with More ❯

and maintain continuous pipelines: ingest 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 (industry or research) with shipped More ❯

/knowledge: Experience in architecting and solutioning in Gen AI, Agentic AI, classic ML, and automation space. Good understanding of Prompt engineering, RAG pipelines, Supervised/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability. Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases (Pinecone, Weaviate, FAISS, etc.). Ability to … services. Strong foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Proficiency in Python with AI/ML frameworks (PyTorch, TensorFlow). Experience with MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). More ❯

deployed systems; Hands-on experience with sensor selection, placement, and calibration. Expertise in training and fine-tuning models for perception in novel or domain-shifted environments; Solid foundation in MLOps, including dataset management, training infrastructure, and deployment pipelines. Nice to have: Experience in construction robotics, heavy machinery, or large-scale manipulation tasks. Familiarity with vision-language models and their use More ❯

data ecosystem. You’ll be the technical lead for machine learning and AI engineering — building production-ready systems, enabling seamless collaboration with data scientists, and shaping the long-term MLOps strategy. Beyond implementation, you’ll play a pivotal role in defining how advanced analytics supports smarter decision-making, better customer experiences, and more sustainable operations across the business. What You … and high performing. Work hand-in-hand with data scientists to design, prototype, and operationalize ML and AI models that deliver real business value. Develop and maintain a comprehensive MLOps framework — from versioning and CI/CD to monitoring and governance. Provide technical guidance and mentorship, helping grow a capable ML Engineering team over time. Partner with product, platform, and … can translate technical complexity into business value. Proven experience in classical machine learning, with hands-on expertise in model development, optimisation, and deployment. Deep understanding of ML Engineering and MLOps principles (cloud-based pipelines, CI/CD, monitoring, reproducibility). Experience with Python, SQL & Azure (AWS & GCP is also fine). Exposure to GenAI or LLM tools and frameworks is More ❯

integrating with live data feeds and cloud infrastructure. Research and prototype cutting-edge AI techniques (e.g., deep learning, reinforcement learning, generative models). Support continuous model improvement and scalable MLOps deployment pipelines. TECH STACK/REQUIREMENTS Core Skills: Python, TensorFlow/PyTorch, scikit-learn, OpenCV, NumPy, Pandas Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data … BE CONSIDERED... Please apply directly by emailing jordanna.ramsey@searchability.com with your CV and availability. KEYWORDS: AI Engineer, Machine Learning Engineer, Sports Analytics, Computer Vision, Deep Learning, Python, TensorFlow, PyTorch, MLOps, Data Science, Predictive Modelling, Sports Tech, AI in Sports More ❯

of analytical tools, frameworks, and environments used for data science, including reproducibility and collaboration readiness. • Benchmarking: Define benchmarks for best practices in experimentation, automation, and applied machine learning operations (MLOps). • Collaboration & Alignment: Work with AI and Data & AI Architects to connect findings from people, platform, and practice assessments into a unified capability map. • Gap Identification: Identify gaps in model … Qualifications • 6–10 years of experience in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g. More ❯

of the chemical and energy supply chain. Build agent-based systems that perform complex automated tasks, updating the digital twin based on real-time data. Establish the foundations for MLOps and performance monitoring. Design and build robust, scalable ETL/ELT pipelines to ingest large volumes of data from APIs, web scraping, and multiple data sources. Own the scalability, reliability … technologies (AWS, Azure, or GCP). Expert knowledge of Python and SQL Hands-on experiences with Data Architecture, including: Cloud platforms and orchestration tools (e.g. Dagster, Airflow) AI/MLOps: Model deployment, monitoring, lifecycle management. Big Data Processing: Spark, Databricks, or similar. Bonus: Knowledge Graph engineering, graph databases, ontologies. Located in London And ideally you... Are a zero-to-one More ❯

hyper-personalise every shopper touchpoint. As we scale from research to production, we need robust infrastructure that makes our models reliable, reproducible, and observable at scale. 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 that allow our Research Engineers More ❯

Quantitative Developer – Trading – MLOps/Python A hedge fund is building out their AI capability and have an opportunity for a quantitative developer to play a key role in building out MLOps workflows and pipelines for the trading desks. This role is ideally suited to a software engineer or quantitative developer with experience delivery solutions directly for trading desks, who … has excellent Python skills, with a solid background in one of Java/C C#, who has experience building MLOPs pipelines for data scientists, AI engineers, quants, traders and leadership, to build strategic systems and enhance production systems. You should apply for this role if you are/have: 10+ years software engineering/quantitative development within financial markets Excellent … Python (NumPy, PyTorch, TensorFlow, Scikit); solid OO background in C++, Java or C# Strong MLOps and AI/ML model lifecycle experience Strong financial product knowledge and experience delivering solutions for trading/pricing Degree educated or higher in a relevant discipline from a leading academic institution This is an £800-900/day PAYE role based London initially for More ❯

🚀 AI Engineer (LangGraph Expert) Salary: £90k - £130k + Equity + Bonus Location: Remote/Hybrid in London We’re looking for a brilliant AI Engineer who’s passionate about building intelligent, agentic systems using LangGraph. If you thrive at the More ❯

A global lifestyle brand is hiring a Data Scientist to join the team. You will report to the Director of Global Customer Data Science and work on developing predictive models and customer segmentation strategies to enhance personalised experiences and improve More ❯

pipelines using LLMs and transformer-based architectures. Analyse unstructured data, including medical text and images. Develop predictive models for underwriting decisions. Implement automated ML workflows, CI/CD, and MLOps practices. Architect APIs for model integration and external system interaction. Monitor performance to ensure scalable, reliable production deployments. What We’re Looking For: Essential: Experience with LLMs (GPT, BERT) and … for domain-specific applications. Strong Python skills (OOP, PyTorch, Hugging Face, scikit-learn, Pandas, NumPy). Deploying models with Docker, Kubernetes, or serverless platforms. Familiarity with CI/CD, MLOps, and cloud platforms (AWS preferred). Desirable: Named entity recognition, recommendation systems, or image analysis. Knowledge of Java (Spring Boot) or GitLab/GitHub CI/CD. Why Join? Lead More ❯

LLM models; build LLM apps with LangChain/LangGraph; apply multimodal AI, prompt engineering, fine-tuning, and model optimization; ensure scalable, reliable, and business-aligned AI solutions. - Platform & Operations (MLOps): Deploy and operate services on Azure; implement CI/CD and Infrastructure as Code; add monitoring, logging, and observability; ensure reliability, fault tolerance, and performance optimization. - Governance & Security: Enforce data … integration skills. - Experience with ML/LLM frameworks (PyTorch, TensorFlow, scikit-learn, LangChain/LangGraph, LlamaIndex). - Familiarity with cloud deployment (Azure), CI/CD, Infrastructure as Code, and MLOps best practices. - Knowledge of databases, vector search, and knowledge graphs. - Understanding of security, compliance, and ethical AI. Personal Attributes: - Passionate, pragmatic, and self-driven with strong technical leadership. - Able to More ❯

Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI/CD and model monitoring to cost and performance optimisation. Partnering with Data Engineers to ensure the pipelines and data structures feeding the models are …/ML or LLM-driven products. Experience with cloud ML platforms (AWS SageMaker, Bedrock, Vertex AI, etc.). Familiarity with LangChain or similar orchestration frameworks. A solid understanding of MLOps and how to take models from concept to production. A collaborative mindset - you enjoy working closely with Product and Data teams and mentoring others when needed. Curiosity, experimentation, and a More ❯

building data-centric APIs using FastAPI , especially in serverless environments (e.g., Google App Engine) Streaming Data: Familiarity with real-time data pipelines and data ingestion at scale DevOps/MLOps: Exposure to Terraform , Docker , Kubernetes , and MLOps workflows What They Offer A chance to work on real-world data that impacts elite football performance Access to high-value datasets , sports More ❯

A global lifestyle brand is hiring a Data Science Manager to lead personalisation efforts within its CRM ecosystem. The role sits in the Consumer Intelligence and Experience (CIX) team, which drives customer engagement through predictive analytics and insights across all More ❯

Role As our first senior AI hire, you’ll take ownership of designing and building the foundation of our AI ecosystem. You’ll shape the infrastructure strategy, establish our MLOps pipelines, and work closely with product and data teams to enable seamless model development and deployment. Once the environment is established, you’ll play a key role in recruiting and … mentoring two mid-level AI engineers who will join your team. Responsibilities Architect, build, and maintain an in-house AI environment (on-prem or hybrid cloud). Design MLOps workflows for training, deploying, and monitoring models. Integrate and manage containerized AI engines (Docker/Kubernetes). Establish best practices for model versioning, data pipelines, and reproducibility. Collaborate with ML and … or on-premise platforms (non-cloud environments). Solid understanding of data pipelines, APIs, and scalable system architecture. Preferred Experience leading small teams or mentoring other engineers. Familiarity with MLOps tools and best practices. Background in integrating AI solutions into enterprise products. Awareness of privacy, bias mitigation, and model explainability techniques. What We Offer Opportunity to design and own the More ❯

. Develop pipeline programming using Python, Spark, and SQL; integrate APIs for seamless workflows. Support Machine Learning and AI initiatives, including NLP, Computer Vision, Time Series, and LLMs. Implement MLOps, CI/CD pipelines, data testing, and quality frameworks. Act as an AI super-user, applying prompt engineering and creating AI artifacts. Work independently while providing clear justification for technical … Proficient with cloud platforms (Snowflake, AWS fundamentals). Solid understanding of data architecture, warehousing, and modeling. Programming expertise: Python, Spark, SQL, API integration. Knowledge of ML/AI frameworks, MLOps, and advanced analytics concepts. Experience with CI/CD, data testing frameworks, and versioning strategies. Ability to work effectively in multi-team, vendor-integrated environments. Why This Role Join a More ❯

🔍 ML Ops Engineer – Cloud, Data, and AI Are you passionate about shaping the future of data and cloud technology? We’re partnering with a forward-thinking financial organization investing heavily in its data and AI capabilities — and they’re looking More ❯

MLOps Engineer Contract London based (hybrid or onsite depending on preference) Outside IR35 £600 to £650 per day I am supporting a client in London who is preparing to scale their machine learning capability. They need an experienced MLOps Engineer who can take models from development into a reliable production environment and keep everything running smoothly throughout the lifecycle. What … stability and performance across the ML platform. Key skills • Strong Python for production environments. • Solid experience with Azure for ML workloads including compute, networking and storage. • Good knowledge of MLOps tools such as MLflow, Azure ML, Kubeflow or similar. • Strong background in CI and CD using GitHub Actions, Azure DevOps or similar. • Experience working in containerised environments using Docker and … have • Experience working on large scale data or ML projects. • Good understanding of data engineering fundamentals. • Experience working in regulated or high availability environments. If you're an experienced MLOps Engineer feel free to apply or send your C.V to nokakpu@revoco-talent.co.uk MLOps Engineer More ❯

powered solutions . Lead on data strategy , from collection and simulation to training and validation. Ensure AI safety, ethics, explainability , and compliance with defence standards. Define best practices in MLOps , model evaluation, and continuous integration. Essential Skills & Experience MSc (or PhD) in Computer Science, Artificial Intelligence, Machine Learning, Robotics, or related engineering field . 5+ years of experience in AI … or sensor fusion . Excellent communication and problem-solving skills. Desirable Extras Publications in leading AI/ML or robotics conferences (e.g. NeurIPS, CVPR, ICLR, ICRA). Experience with MLOps pipelines, synthetic data, and model optimisation for edge devices . Understanding of sensor fusion, perception pipelines , and AI safety principles . What’s on Offer Highly competitive base salary. Generous More ❯