76 to 100 of 120 MLOps Jobs in Central London

Senior DevOps Engineer (MLOps/LLMOps) Clearance: Eligible for BPSS Start: ASAP Work pattern: Hybrid (London) Work type: 12 month FTC (Competitive Salary) We’re working with a major UK government initiative that’s shaping the future of how citizens interact with public services. The programme focuses on harnessing the latest AI technologies to deliver simpler, faster, and more efficient … Azure, or GCP) using Python-based Infrastructure as Code (Terraform or Pulumi). Build and optimise CI/CD pipelines to automate the deployment of AI applications and models (MLOps/LLMOps). Containerise workloads using Docker and manage deployments through Kubernetes (KubeRay, Kubeflow, MLRun, or similar). Provision and manage the infrastructure required to run AI workloads, including vector … Experience Strong background as a DevOps or Cloud Engineer in public cloud environments (AWS, Azure, or GCP). Experience deploying and managing infrastructure for AI/ML workloads using MLOps or LLMOps practices. Excellent scripting and automation skills in Python (e.g. Boto3, SDKs). Proven experience with Python-based IaC frameworks (Pulumi, Terraform, CDKs). Hands-on experience building CI More ❯

Science Engineer to design, develop, and 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. Clean, testable code with … years in production-level AI/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-solving, and stakeholder engagement. More ❯

Science Engineer to design, develop, and 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. * Clean, testable code with … years in production-level AI/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-solving, and stakeholder engagement. In More ❯

and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). … loading (ETL). Experience with SQL databases (SQL Server), data validation, cleansing workflows, scheduling tools (Azure Data Factory), and ensuring data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing CI/ More ❯

and deploy production-grade AI systems tailored to audit applications. Contribute to architectural decisions and solution governance. Write clean, efficient, and scalable code that adheres to software engineering principles, MLOps practices, and cloud-native development. AI Solution Delivery: Own the implementation of ML pipelines, APIs, and data integration workflows. Operational Excellence: Contribute to defining reusable development patterns, enforcing coding standards … and implementing MLOps best practices that support version control, performance optimisation, and maintainability. Cross-Disciplinary Collaboration: Work side-by-side with data scientists, product managers, platform engineers, and QA teams to align on technical requirements, delivery timelines, and integration plans. Provide hands-on contributions to ensure AI capabilities are smoothly embedded within core audit platforms and services. Capability Building & Knowledge More ❯

ALTEN , an engineering and technology consultancy, We are a leading Engineering and IT consultancy operating across 30 countries, making waves in all sectors: Aeronautics, Space, Defence, Security and Naval, Automotive, Rail and mobility, Energy and environment, Life Sciences and health More ❯

and scales AI, including generative and agentic technologies, end-to-end. Skills and Experience Proven leadership experience in Gen AI delivery Hands-on technical understanding of modern AI tooling, MLOps, and model deployment and generative/agentic AI frameworks Commercial mindset, experience bringing AI, including generative, use cases into production Strong communication skills and the ability to build and scale More ❯

ML models Able to be SC cleared - Must hold British passport (we have MoD projects) There are also some things that would be helpful to have like experience with MLOps and having worked in a startup. What do we offer? Upto £140k base salary Generous share options Flexible and hybrid working – 2 days in office (40 mins from Paddington) Interested More ❯

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 ❯