26 to 50 of 110 MLOps Jobs in the City of London

natural problem solver with curiosity for cutting-edge tools and frameworks Desirable Experience Experience delivering AI or ML solutions in a commercial or consultancy setting Knowledge of data engineering, MLOps, or automation workflows Previous experience engaging directly with clients and senior stakeholders Why Join This is an opportunity to join a fast-scaling AI business at a defining stage of More ❯

environments (e.g. AWS, GCP, or Azure) Exposure to modern data tools such as Airflow, dbt, or Snowflake Experience or strong interest in streaming technologies like Apache Kafka Interest in MLOps and modern data engineering best practices Why join: You’ll be part of a company with a clear mission and strong data culture, joining a team that values learning, collaboration More ❯

and ability to work cross-functionally. Desirable MSc/PhD in Machine Learning, Computer Vision, or related field. Knowledge of Generative AI models (GANs, diffusion, LLMs). Experience with MLOps and lifecycle management. Experience in startups or fast-paced R&D. Why apply? Flexible hybrid working (London office) A chance to shape the future of AI in sport and human More ❯

deployment of predictive and optimisation models across multiple industries Own the end-to-end ML pipeline: data exploration, feature engineering, modelling, evaluation, and deployment Collaborate with data engineers and MLOps professionals to ensure scalable, production-grade solutions Conduct rigorous A/B testing and performance measurement to validate impact Act as a technical lead within project teams, mentoring mid-level More ❯

findings into actionable insights for coaches, analysts, and execs Highly Desirable Skills Football Analytics Experience : Familiarity with football-specific datasets (event, tracking, positiona l), and libraries like mplsoccer Advanced MLOps & Modelling : Experience with the Vertex AI ecosystem, especially pipelines, and advanced techniques such as player valuation , tactical modelling , etc. Bayesian Modelling : Knowledge of probabilistic programming (e.g., PyMC ) for uncertainty-aware More ❯

ML models. Partner with data engineers, ML engineers, architects, and business teams to shape ML initiatives. Present insights clearly through strong data visualisation and storytelling. Uphold software engineering and MLOps best practices (testing, versioning, quality, automation). Contribute to governance, responsible model usage, and data quality standards. Mentor juniors and support code reviews. Required Experience You’ll need: Strong Python More ❯

Azure platform. You will work closely with data scientists, researchers, and product managers to translate complex analytical models into production-ready, scalable applications. Your expertise in Azure AI services, MLOps, and data engineering will be crucial in delivering high-impact AI capabilities. Responsibilities • Design & Development: Architect and develop end-to-end AI/ML solutions using Azure AI services (e.g. … Azure Machine Learning, Azure Cognitive Services, Azure Databricks, Azure Synapse Analytics). • Model Deployment & Management: Implement robust MLOps practices for continuous integration, continuous delivery (CI/CD), monitoring, and retraining of machine learning models in Azure. • Data Pipelining: Build and optimize scalable data pipelines for ingesting, transforming, and preparing large datasets for AI model training and inference. • Performance Optimization: Optimize … Documentation: Create comprehensive documentation for AI solution architectures, deployment procedures, and operational guidelines. • Stay Current: Keep abreast of the latest advancements in Azure AI services, machine learning technologies, and MLOps trends. Qualifications we seek in you! Minimum Qualifications • Bachelor's or master’s degree in computer science, Engineering, Data Science, or a related quantitative field. • Relevant years of experience in More ❯

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000 About the Company Our client is an extremely well know, digital marketplace focused on sustainable ecommerce. With over 35 million of active users globally, they’re redefining how people buy and sell second-hand fashion, aiming to make the future … The organisation champions diversity, equal opportunity, and flexible working. They offer a progressive benefits package designed to support wellbeing, learning, and work-life balance. The role of Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000 Our Client is looking for an experienced MLOps Engineering Manager to lead and … scale their MLOps function. You will be shaping how machine learning is developed, deployed, and scaled across the organisation. This is a genuinely high-impact role: you’ll lead a talented team of 6-8 engineers, set the strategic direction for ML infrastructure, and ensure the business continues to deliver reliable, scalable, and high-performing ML systems that drive real More ❯

TOGAF, Zachman) and cloud-native architecture (preferably Microsoft Azure). · Understanding LLMs and Python-based agentic frameworks like LangChain or AutoGen. · Strong understanding of data governance, security protocols, and MLOps practices in a cloud environment. · Proven track record of designing and deploying scalable AI solutions that deliver measurable business value. · Excellent communication, leadership, and analytical skills, with the ability to More ❯

with the flexibility for some work from home. The AI Engineer will support delivery of AI use cases, workflows, analytics and solutions - responsible for tasks such as pattern recognition, MLOps, software development, neural networks, and natural language processing on a day-to-day basis. Qualifications Pattern Recognition and Neural Networks skills Computer Science and Software Development skills Natural Language Processing More ❯

mining, or similar). Proven track record of building or deploying GenAI/LLM systems into production. Strong Python skills, with experience in PyTorch or TensorFlow . Understanding of MLOps practices (evaluation, monitoring, scaling). Master’s (or equivalent experience) in Computer Science, AI, ML, or related field. ⭐ Bonus points for: experience designing annotation schemas, Kaggle Master credentials, or published More ❯

the machine learning strategy and direction for assigned projects. Lead the design, training and validation of models and algorithms. Maintain hands-on involvement. Work collaboratively with data engineers (note: MLOps responsibilities are handled by the engineering team). Contribute to broader AI and ML strategy development within the engagement. Required Skills & Experience 6+ years’ experience in Data Science. Demonstrated experience More ❯

bonus Principal: £120,000–£140,000 base + ~20%+ bonus Team : 3–4 person AI team within a 100-person private equity firm Tech Stack : Python, LLMs, MLOps, cloud (Azure, AWS, GCP), agentic systems Visa Sponsorship : Happy to sponsor Interested? Please apply below. More ❯

deployment of predictive and optimisation models across multiple industries Own the end-to-end ML pipeline: data exploration, feature engineering, modelling, evaluation, and deployment Collaborate with data engineers and MLOps professionals to ensure scalable, production-grade solutions Act as a technical lead within project teams, mentoring mid-level data scientists and guiding model design choices Communicate findings and strategic insights More ❯

Extensive experience with ML & DL platforms, frameworks, and libraries 📚 Extensive experience with end-to-end model design and deployment within cloud environments ☁️ A systems thinking approach 🌐, with passion for MLOps best practises 🌀 An engineer that can think in O(n) as much as plan the orchestration of their product. Solid understanding of data structures, data modelling, and software architecture, especially More ❯

other geospatial tooling experience. Experience with AWS and containerised environments (Docker). Familiarity with backend frameworks (FastAPI, Flask, or Django). Experience working with data pipelines, ML workflows, or MLOps-style environments. Ideally some experience with TypeScript for visualisation or frontend-adjacent tooling. Comfort navigating ambiguity, proposing improvements, and balancing short-term execution with long-term architecture. Strong communication skills More ❯

and speaking with stakeholders where required • Someone who enjoys solving real problems end-to-end: from understanding the domain → designing the approach → shipping the implementation • Experience of MLFlow/MLOps highly desirable Important notes (just to be upfront): • They cannot sponsor visas, so you’ll need existing right to work in the UK • No relocation support – you’ll need to More ❯

e.g., NumPy, pandas, scikit-learn, PyTorch) and open-source contributions (especially Python-based) would be a bonus. Familiarity with CUDA, GPU-based computations, end-to-end neural network training, MLOps, and academic research in machine learning are also beneficial. Experience configuring and maintaining cloud infrastructure including network infrastructure, compute, access control policies, load balancers, Public Key Infrastructure (PKI), and DNS. More ❯

Senior Data Scientists/Analysts – SC/DV Cleared – (Military/Veteran-Friendly) — Multiple Openings 🌳 Location: London (Remote & Hybrid options) 🌳 Employment: Permanent 🌳 Clearance: Current SC or DV required 🌳 Make sense of mission-critical data that actually drives decisions. 🌳 Areti Group More ❯

Senior MLOps Engineer | Established Bank in London | £90,000-£ 95,000 base + Bonus | Hybrid We are looking for an experienced MLOps Engineer in London to join an established Banking client who have multiple offices around the UK. They have allocated a ton of investment to propel their AI & Cloud transformation and this role will be the first of its … environments Automate pipelines and services (ETL, storage, databases) Collaborate with data scientists and engineers Explore new tools to boost ML performance and reliability What are we looking for? Solid MLOps or ML Engineering experience Strong Python & SQL skills Hands-on with AWS, Azure, Terraform Great communication & problem-solving skills Bonus: Familiarity with finance or data viz tools (Power BI/ More ❯

Bring Experience delivering and scaling enterprise AI/ML systems Hands-on expertise with cloud AI platforms (AWS, Azure, etc.) Strong Python engineering skills + FastAPI, CI/CD, MLOps, LLM tooling Leadership across cross-functional engineering teams If you are Interested, apply directly or reach out to jonah.brookes@oliverbernard.com Principal AI Engineer | Up to £130K | Hybrid in London More ❯

Jupyter notebooks A practical, problem-solving mindset with a focus on clean, reliable delivery Nice to have: Experience with R or other statistical tools Exposure to model deployment/MLOps environments Familiarity with high-volume digital products or gaming/sports analytics More ❯

Bring Experience delivering and scaling enterprise AI/ML systems Hands-on expertise with cloud AI platforms (AWS, Azure, etc.) Strong Python engineering skills + FastAPI, CI/CD, MLOps, LLM tooling Leadership across cross-functional engineering teams If you are Interested, apply directly or reach out to jonah.brookes@oliverbernard.com Lead AI Engineer | £90K-£110K + Bonus | Hybrid in London More ❯

learning, deep learning or AI engineering • Advanced proficiency in Python and ML frameworks like PyTorch • Experience deploying and maintaining scalable production-grade AI systems • Strong understanding of data pipelines, MLOps and cloud platforms (AWS) • Experience working in small or mid-sized tech/software development environments (rather than in corporate environments) would be advantageous The role requires demonstrable ability to More ❯

to drive AI/ML innovation while ensuring models are scalable and impactful. What You’ll Be Doing: Developing predictive models to forecast property trends and customer behavior. Leveraging MLOps best practices to ensure efficient model deployment and monitoring. Working end-to-end across the data pipeline – from data extraction to model evaluation. Collaborating with cross-functional teams to turn More ❯