51 to 75 of 137 MLOps Jobs in the UK

a technical lead role. Deep experience in training and deploying computer vision models into production Proven track record with LLM fine-tuning, prompt engineering and productionisation Deep experience in MLOps on Azure, including CI/CD, monitoring and scaling pipelines. Strong coding skills in Python, with frameworks such as PyTorch, FastAPI and Azure CLI. ALL APPLICANTS MUST BE FREE TO More ❯

a technical lead role. · Deep experience in training and deploying computer vision models into production · Proven track record with LLM fine-tuning, prompt engineering and productionisation · Deep experience in MLOps on Azure, including CI/CD, monitoring and scaling pipelines. · Strong coding skills in Python, with frameworks such as PyTorch, FastAPI and Azure CLI. ALL APPLICANTS MUST BE FREE TO More ❯

a technical lead role. · Deep experience in training and deploying computer vision models into production · Proven track record with LLM fine-tuning, prompt engineering and productionisation · Deep experience in MLOps on Azure, including CI/CD, monitoring and scaling pipelines. · Strong coding skills in Python, with frameworks such as PyTorch, FastAPI and Azure CLI. ALL APPLICANTS MUST BE FREE TO More ❯

findings to non-technical stakeholders. Highly desirable skills include: Football Analytics Domain: Significant plus if experienced with football datasets (event, tracking, etc.) and visualization libraries like mplsoccer . Advanced MLOps & Modelling: Deeper experience with the Vertex AI lifecycle (especially Pipelines ) and advanced modelling techniques relevant to football (player valuation, tactical analysis). Bayesian Modelling: Experience with probabilistic programming (e.g., PyMC More ❯

models, dimension reduction, clustering, classification, regression techniques, etc.) Familiarity with PyTorch, PyTorch Lightning, or similar frameworks Strong coding abilities in Python Strong Software development skills and familiarity with GPUs, MLOps, Git, High-performance large-scale ML systems and platforms. Experience with Transformers, LLMs, NLP, Multi-modal Deep Learning, and VLMs/MLLMs Publication track record in machine learning conferences and More ❯

model strategy, adaptation, and LLMOps. AI safety, governance, and compliance-especially on Azure. Machine Learning at Scale Applied ML: time-series forecasting, tabular ensembles, NLP, recommendation/propensity models. MLOps: automated pipelines, registries, scoring, monitoring, and safe rollbacks. Responsible ML: bias/fairness testing, explainability, PII controls, human-in-the-loop, EU AI Act readiness. Experimentation: rigorous testing, telemetry, and More ❯

Architecture Govern technical decisions around cloud compute, security, networking, and DevOps practices. Ensure cloud-native scalability, monitoring, and resilience of the AI-driven quoting platform. Oversee the adoption of MLOps pipelines (i.e., HeatWave AutoML + OCI Data Science). Define architectural standards and enforce architecture reviews across all technical workstreams. Governance & Leadership Run the Architecture Review Board (ARB) for the More ❯

architectures. Providing technical leadership to drive up levels of technical expertise and best practice across the Machine Learning discipline, leading by example and mentoring others. Working closely with our MLOps team to steer the ongoing development of tools to enable rapid iteration of models and optimisations of the full ML model lifecycle. You should apply if What we're doing More ❯

architectures. Providing technical leadership to drive up levels of technical expertise and best practice across the Machine Learning discipline, leading by example and mentoring others. Working closely with our MLOps team to steer the ongoing development of tools to enable rapid iteration of models and optimisations of the full ML model lifecycle. You should apply if What we're doing More ❯

initiatives. Experience required: AWS Data/ML Engineering & ML Ops (ECS, Sagemaker) CI/CD pipelines (GitLab, Jenkins) Python, PySpark & Big Data ecosystems AI/ML lifecycle, deployment & monitoring MLOps tooling (MLflow, Airflow, Docker, Kubernetes) Front-end exposure (HTML, Flask, Streamlit) RESTful APIs & backend integration If this ML Engineer role is of interest, please apply now for immediate consideration. More ❯

Build and scale pricing models to drive revenue and customer value Lead experimentation (A/B, MAB, causal inference) and analytics projects Set best practices in data science and MLOps Mentor and manage a UK/US-based team Communicate insights and partner with product, engineering, and commercial teams What We're Looking For 10+ years in data science; 5+ More ❯

as OpenAI, LangChain/LangGraph, LlamaIndex Experience with Hugging Face and LoRA/QLoRA for fine-tuning Experience with RAG & Vector DBs eg. FAISS, Weaviate, Pinecone Any experience of MLOps with MLFlow, AWS (SageMaker), CI/CD (GitHub Actions) or similar would be a benefit to an application The employer is well known not only for the forward-thinking approach More ❯

ML architecture and system design (minimum 2 years) with proven expertise in hands-on coding AI solutions. Deep knowledge of machine learning frameworks, cloud platforms (AWS, Azure, GCP), and MLOps(machine learning operations) practices. Deep knowledge about design and implementation of secure integrations between AI solutions and legacy enterprise software, whilst ensuring compliance with government-level security requirements, implementing single More ❯

so your impact will be visible, measurable, and far-reaching. You'll also play a key role in modernising and streamlining the platform's AI and data infrastructure, championing MLOps best practices, and aligning technical delivery with strategic goals. What You'll Bring: Significant experience leading AI/ML teams in fast-paced, SaaS-driven environments Proven ability to deliver More ❯

so your impact will be visible, measurable, and far-reaching. You'll also play a key role in modernising and streamlining the platform's AI and data infrastructure, championing MLOps best practices, and aligning technical delivery with strategic goals. What You'll Bring: Significant experience leading AI/ML teams in fast-paced, SaaS-driven environments Proven ability to deliver More ❯

and/or C++ Development of distributed systems Kubernetes (K8s) AWS (SQS, DynamoDB, EC2, S3, Lambda) Apache Spark Performance testing Bonus Search system development (indexing/runtime/crawling) MLOps development and/or operations The cash compensation range for this role is $80,000 - $160,000. At Perplexity, we've experienced tremendous growth and adoption since publicly launching the More ❯

a related field Experience with multi-modal models that combine vision and language Strong grasp of data-centric AI practices - annotation tooling, prompt evaluation, and dataset curation Familiarity with MLOps tools (e.g. Weights & Biases, SageMaker, MLflow) Experience working in regulated sectors like insurance, banking, or property What You'll Be Doing This is a hands-on, high-impact role - you More ❯

a related field Experience with multi-modal models that combine vision and language Strong grasp of data-centric AI practices - annotation tooling, prompt evaluation, and dataset curation Familiarity with MLOps tools (e.g. Weights & Biases, SageMaker, MLflow) Experience working in regulated sectors like insurance, banking, or property What You'll Be Doing This is a hands-on, high-impact role - you More ❯

Senior Data Engineer (AI & MLOps) - Software - Newcastle/Hybrid or Remote Day rate: £300 - £500 (Inside IR35) Duration: 6 months Start: ASAP My new client is looking for a Senior Data Engineer with expertise in AI, MLOps, and AWS architecture to design and deliver production-grade machine learning pipelines. The ideal candidate will be passionate about bridging the gap between … compliance of data pipelines and deployed ML solutions. Mentor junior engineers and contribute to setting technical standards for the team. Required Qualifications Proven experience as a Senior Data Engineer, MLOps Engineer, or similar role. Strong background in data structures, algorithms, and software engineering principles. Advanced proficiency in Python for data wrangling, pipeline automation, and ML workflows. Expertise in AWS services More ❯

seek an adept expert to contribute significantly to our R&D team, bridging machine learning engineering with applied data science. You'll improve and manage our Machine Learning Operations (MLOps) on Azure, and participate in creating, assessing, and advancing various machine learning models and AI systems. Collaborate extensively with scientific and operational teams to guarantee the robustness, scalability, and reliability … informed decision-making and boost innovation. Help our CDMO's mission by turning research insights into practical solutions efficiently. Key responsibilities: Compose, construct, and uphold resilient machine learning operations (MLOps) pipelines that facilitate the complete lifecycle of AI modelsfrom creation to implementation and supervision. Guarantee the successful deployment of machine learning and large language models (LLMs) in practical operational settings … interpret, share, and act on. Key requirements: MSc or BSc in Computer Science, Data Science, Bioinformatics, Engineering, or a related field, or equivalent experience. Proven experience designing and deploying MLOps pipelines (MLflow, Azure ML, Azure DevOps etc). Strong programming skills in Python and familiarity with common ML/AI libraries (scikit-learn, tensorflow, Keras etc.). Experience implementing machine More ❯

maintain secure AWS cloud infrastructure Implement and manage CI/CD and DevOps pipelines Champion data quality, security, and best practices Collaborate with cross-functional teams Implement and manage MLOps capabilities Essential Skills: Advanced Python programming skills Expertise in data engineering tools and frameworks (Apache Flink) Hands-on AWS experience (Serverless, CloudFormation, CDK) Strong understanding of containerization, CI/CD More ❯

reinforcement learning Model selection, training, validation, and evaluation Deep learning architectures (CNNs, RNNs, Transformers) Data Science Statistical analysis, feature engineering, A/B testing Experiment design and hypothesis testing MLOps & Engineering Scalable ML systems (batch and real-time) ML pipelines, CI/CD, monitoring, deployment Familiarity with tools like MLflow, Kubeflow, Airflow, Docker, Kubernetes Strategic skills Align ML initiatives with More ❯

ML architecture and system design (minimum 2 years) with proven expertise in hands-on coding AI solutions. Deep knowledge of machine learning frameworks, cloud platforms (AWS, Azure, GCP), and MLOps(machine learning operations) practices. Deep knowledge about design and implementation of secure integrations between AI solutions and legacy enterprise software, whilst ensuring compliance with government-level security requirements, implementing single More ❯

ML architecture and system design (minimum 2 years) with proven expertise in hands-on coding AI solutions Deep knowledge of machine learning frameworks, cloud platforms (AWS, Azure, GCP), and MLOps(machine learning operations) practices. Deep knowledge about costing of AI models to produce reasonable ROI and modular reusable services with moderate level of maintenance Understanding of data architecture, API design More ❯

ML architecture and system design (minimum 2 years) with proven expertise in hands-on coding AI solutions. Deep knowledge of machine learning frameworks, cloud platforms (AWS, Azure, GCP), and MLOps(machine learning operations) practices. Deep knowledge about design and implementation of secure integrations between AI solutions and legacy enterprise software, whilst ensuring compliance with government-level security requirements, implementing single More ❯