76 to 100 of 202 MLOps Jobs in the UK

Proven experience with ML in production environments Strong skills in Python, SQL, and AWS (SageMaker, Lambda, etc.) Experience with NLPs or LLMs Familiarity with MLOps and real-time data applications A pragmatic mindset and strong problem-solving ability Background in adtech or fraud detection is a bonus ...

LLMs, NLP, or retrieval-augmented workflows. * Confident working with large-scale tabular datasets and complex data environments. * Cloud experience, ideally AWS, plus familiarity with MLOps practices. * Background in product-focused environments or consultancy. * Ability to evidence measurable business impact from previous technical projects. What They Offer * Hybrid working with ...

Observability Vulnerability scanning, IDAM, secure networking Tools like Prometheus, Grafana, ELK What makes this role different: Work on cutting-edge defence challenges (DevSecOps + MLOps + secure cloud) Influence technical direction, not just delivery Solve problems like securely updating deployed equipment in the field Be part of a culture that ...

working in cloud environments, ideally Azure. Solid understanding of software engineering fundamentals including APIs, distributed systems and secure system design. Experience with DevOps or MLOps practices such as CI CD, containerisation and monitoring. The confidence and gravitas to influence technical direction and guide a less mature AI function. A practical ...

related field Experience with genomics pipelines or tools (e.g. FASTQ, BAM, variant calling) Knowledge of protein structure modelling or drug discovery workflows Exposure to MLOps, deployment, and production systems Publications or open-source contributions in relevant domains Why Join Them Opportunity to work on genuinely impactful problems in healthcare ...

automation Containerisation and orchestration Monitoring and observability tools Experience in the defence or advanced technology sector Experience with GPU based computer environments Experience with MLOps and associated tooling Experience with data pipelines Experience with Infrastructure as Code Experience with security integration in DevOps i.e. DevSecOps Service-oriented with effective communication ...

automation Containerisation and orchestration Monitoring and observability tools Experience in the defence or advanced technology sector Experience with GPU based computer environments Experience with MLOps and associated tooling Experience with data pipelines Experience with Infrastructure as Code Experience with security integration in DevOps i.e. DevSecOps Service-oriented with effective communication ...

based and agent-driven AI systems using secure, governed data sources Develop scalable services on Azure, integrating with enterprise platforms and data pipelines Implement MLOps and AIOps capabilities including model lifecycle management, monitoring, and automation Embed security, compliance, and safety principles into every stage of development Collaborate within cross-functional ...

Preferred Experience with multimodal reasoning Experience with edge or offline AI deployments Familiarity with Kubernetes (EKS/OpenShift) for monitoring and managing deployed applications MLOps experience - model evaluation, monitoring, reproducibility Observability tooling for agentic systems (model drift, agent behaviour, performance monitoring) Experience with agent orchestration patterns and inter-agent communication ...

keeps us honest and true to who we are, and above all, it's the reason we all love to work here. Description Senior MLOps Engineer Overview of job This role is part of our Global:IQ team, the group developing our new intelligence platform. Global:IQ brings together … creatives and automates the tracking of outcomes for advertisers through the acquisition funnel-from building awareness and consideration to driving action. As a Senior MLOps Engineer you will play a critical role in building the operational infrastructure that brings AI and ML models into production at Global:IQ. You will ...

Location: Manchester Working: 3 days onsite (Tues-Thurs) Salary: £130k + extensive package (TC circa £200k) The Role Leading multiple teams of Data, DataOps & MLOps Engineers within a global asset manager's European Data & Analytics function. You'll own the data engineering strategy, driving the build of AI-ready, cloud ...

stakeholders to prioritise backlogs, manage risks, and translate requirements into actionable technical plans. Establish and enforce engineering best practices -including coding standards, DevOps/MLOps pipelines, testing frameworks, and documentation-to ensure high-quality delivery. Drive innovation by evaluating emerging data and AI technologies, leading proofs of concept, and embedding ...

AI. Build POVs and architecture options that balance business value, feasibility, and cost. Act as a trusted advisor for our clients across data platforms, MLOps, Generative and Agentic AI, and application design. Thought Leadership, GTM & Vertical Expertise Represent Datatonic on site at client, at events, conferences, partner sessions, and executive ...

including vector embeddings , rerankers , and data indexing in databases like MongoDB. Practical knowledge of LLM fine-tuning, prompt engineering, and performance optimization. Familiarity with MLOps principles and tools, including CI/CD with GitHub Actions . A strong interest in computer vision and an understanding of object detection models like ...

. Familiarity with cloud-native data architectures and technologies. Desirable: Experience with data management tools and processes. Experience with AI and Machine Learning, including MLOps practices. Experience with decentralised Data Mesh and Data Product architecture principles. What you will be doing 1 month: Collaborate with key stakeholders (product managers, engineers ...

value, managing trade-offs, and driving outcomes through a well-managed backlog. Comfortable working with cross-functional analytics teams (analytics engineering, data science, and MLOps) and facilitating collaboration through ambiguity. Pragmatic and delivery-focused, with a risk-based approach to decision-making and a strong bias to measurable business impact. ...

tools such as Terraform, or CloudFormation. Ensure scalability, reliability, and high availability of AI services in production environments Implement and enforce best practices in MLOps, including model versioning, monitoring, rollback, and automated testing. Collaborate with data engineering and ML teams to operationalize AI models and integrate them into business-critical … stack. Contribute to the continuous improvement of DevOps capabilities and tooling within the AI Tech Team. Act as a technical advisor in DevOps and MLOps practices, fostering a culture of automation and engineering excellence. WHAT YOU'LL BRING Our people are our greatest strength. Every individual contributes unique perspectives that ...

guardrails while ensuring business value creation and impact. YOUR ROLE In this position you will play a key part in: Assessing and implementing robust MLOps framework, governance and best practices as per industry standards Designing and delivering end-to-end AI/ML systems, from data preparation and model development … Experience of working with client sponsors, both technical and non-technical, to collaboratively design requirements and build out solutions. Experience of designing and implementing MLOPs strategy and framework and proven track record in designing and delivering AI/ML solutions at scale, from concept to production. Deep understanding of Generative ...

guardrails while ensuring business value creation and impact. YOUR ROLE In this position you will play a key part in: Assessing and implementing robust MLOps framework, governance and best practices as per industry standards Designing and delivering end-to-end AI/ML systems, from data preparation and model development … Experience of working with client sponsors, both technical and non-technical, to collaboratively design requirements and build out solutions. Experience of designing and implementing MLOPs strategy and framework and proven track record in designing and delivering AI/ML solutions at scale, from concept to production. Deep understanding of Generative ...

design patterns, and building scalable, secure systems Working knowledge of software engineering principles and best practices, including testing, packaging, API design, DevOps, DataOps, and MLOps Eligibility to work in the relevant jurisdiction ...

Senior Technology Leader will be responsible for establishing engineering excellence across the organisation. Your remit spans technical standards, cloud native architecture, automation, AI and MLOps, and the evolution of their software engineering culture. You will influence product direction and play a central role in shaping the technical future of their ...

team environment Nice to Have Background in bioinformatics, computational biology, or life sciences Experience with drug discovery, protein modelling, or genomics pipelines Familiarity with MLOps, CI/CD, and scalable cloud infrastructure Exposure to graph-based models, transformers, or multimodal learning Experience in an early-stage startup or fast-scaling ...

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 ...

failure‐mode analysis in production environments. Test Architecture & Automation Leadership Architect scalable, automated test frameworks for AI systems (models, data, pipelines, and integrations), leveraging MLOps, CI/CD, synthetic data, and observability tooling. Technical Leadership & Mentorship Act as the subject‐matter expert for AI testing, mentoring engineers, influencing engineering best ...

technologies. Experience working with cloud platforms, APIs and modern application architectures. Experience of AI platforms operating at enterprise scale. Familiarity with data engineering, MLOps or model lifecycle management practices. Awareness of common enterprise Generative AI patterns (e.g. retrieval‐augmented generation (RAG), prompt and response evaluation, and human‐in‐the‐loop ...