1 to 25 of 44 MLOps Jobs in the North West

Computer Science, Machine Learning, or related field Nice to Have Published research in top ML venues (NeurIPS, ICML, ACL, CVPR). Experience with MLOps tools: MLflow, Weights & Biases, DVC. Familiarity with distributed training frameworks (DeepSpeed, Horovod). Experience with LLM fine-tuning and prompt engineering. Contributions to open-source ...

systems, or complex AI orchestration platforms that go beyond simple LLM integrations Advanced cloud‐native ML infrastructure – Deep expertise building sophisticated ML infrastructure, MLOps pipelines, model serving platforms, and cloud‐native AI systems at scale; experience optimizing cost and performance of production LLM deployments Financial services or analytical domain knowledge ...

systems, or complex AI orchestration platforms that go beyond simple LLM integrations Advanced cloud-native ML infrastructure – Deep expertise building sophisticated ML infrastructure, MLOps pipelines, model serving platforms, and cloud-native AI systems at scale; experience optimizing cost and performance of production LLM deployments Financial services or analytical domain knowledge ...

data science solutions. Ensure sharing best practices and learnings with other squad members. Design and spearhead the development of Gen AI, AI Agents and MLOps platforms, applications, and solutions driven by GenAI, deep learning and machine learning to address business challenges and elevate product performance. Build standards and recommendations ...

workflow optimisation. Cloud and Deployment Knowledge – Familiarity with deploying AI models and AI‐enabled services using cloud platforms such as AWS, Azure, or GCP, MLOps practices, cloud architecture, containerisation, model monitoring, and secure production deployment patterns. Problem‐Solving and Communication skills – Ability to translate complex business and engineering challenges into ...

security capabilities, ensuring health, telemetry completeness, integration fidelity, detection coverage and continuous service improvement; partner across SOC, cloud, identity, data, application security, DevSecOps, MLOps, platform engineering and architecture; deliver executive-ready dashboards and reporting. Governance, Risk and Regulated-Environment Assurance: Translate policy, regulatory and framework expectations into testable requirements ...

data science solutions. Ensure sharing best practices and learnings with other squad members. Design and spearhead the development of Gen AI, AI Agents and MLOps platforms, applications, and solutions driven by GenAI, deep learning and machine learning to address business challenges and elevate product performance. Build standards and recommendations ...

preparation, feature engineering, and feature stores, and understand why reproducibility and point-in-time correctness matter more than they first appear Have worked with MLOps in practice rather than in principle: model versioning and registries, experiment tracking, automated retraining, and monitoring for drift and model degradation (MLflow, SageMaker, Databricks ...

internal stakeholders.** *Excellent coding practice using Python and associated ML packages (HuggingFace, TensorFlow, PyTorch), following best practices.** *Experience developing robust ML pipelines with MLOps tools for model training, monitoring and deployment.** *Experience of appropriate version control and environment management (such as venvs or Docker).** *Experience of writing technical project ...

cases that cement and enhance this belief and deliver a positive impact in the world. Interested? The Fuzzy Labs team are experts on MLOps and in this publication we're going to tell you WTF it is, and why it is essential for building AI applications #J-18808-Ljbffr ...

CreateFuture seeks a Senior Data Engineer with an ML focus to own data and feature pipelines for production ML systems. You will collaborate with data scientists and ML engineers, mentor junior engineers, and elevate engineering ...

You build the runway. Training pipelines, model registries, feature stores, evaluation, deployment, observability and cost governance - you turn ML from artisanal to industrial across every AutoThink product. What you'll build A shared ML/ ...

strong technical expertise across infrastructure automation, environment provisioning, deployment pipelines, containerisation, and cloud platform operations. The role will involve close collaboration with Data Engineers, MLOps Engineers, Solution Architects, and wider technical stakeholders to ensure platforms and pipelines are reliable, secure, scalable, auditable, and aligned with enterprise cloud governance standards. ...

Code (IaC) to support data pipelines and processing workflows on AWS. Design and maintain systems for data version control and reproducible machine learning workflows (MLOps). Work closely with data scientists, analysts and engineers to translate analytical and operational requirements into effective data solutions. Contribute to a collaborative, multidisciplinary environment ...

Code (IaC) to support data pipelines and processing workflows on AWS. Design and maintain systems for data version control and reproducible machine learning workflows (MLOps) . Work closely with data scientists, analysts and engineers to translate analytical and operational requirements into effective data solutions. Contribute to a collaborative, multidisciplinary environment ...

heavily regulated environments. Understanding of secure AI adoption, AI governance, and emerging AI security risks. Exposure to Data Science, Machine Learning Engineering, or MLOps environments. What's on Offer: Opportunity to work on cutting-edge AI and Machine Learning initiatives within a prominent financial institution. Long-term programme with strong ...

Kintec Global Recruitment seeks a DevOps and Machine Learning Operations Engineer to join a specialist software team in Manchester. You will build and operate delivery pipelines, environments, and the model-serving path with IaC, CI ...

generation (RAG) architectures and agent frameworks such as Google ADK and LangChain, helping transform how the Bank serves customers and empowers colleagues. Data engineering, MLOps and intelligent automation, creating resilient data pipelines, scalable deployment architectures and automated machine learning workflows using contemporary engineering and CI/CD practices, enabling … learning AI literacy Ensemble methods & model optimisation Databases & big data Neural networks & deep learning LLM foundations, engineering, customisation and integration Generative and Agentic AI MLOps & cloud computing Your personal learning plan could include Up to three Stanford Artificial Intelligence Professional Programmes Google Cloud certifications Coursera courses on everything from advanced ...

Code (IaC) to support data pipelines and processing workflows on AWS. Design and maintain systems for data version control and reproducible machine learning workflows (MLOps). About You We're looking for a Data or Software Engineer who has experience delivering production data solutions and enjoys solving complex data problems. ...

unsupervised learning, deep learning, transformers, Generative AI or Agentic AI. Experience across areas such as PyTorch, TensorFlow, scikit-learn, Hugging Face, cloud platforms, MLOps, computer vision, NLP, geospatial data or multimodal AI would also be of interest, although candidates are not expected to have experience across every area. Alongside ...

develop expertise across a range of software engineering disciplines, including front-end development, site reliability engineering, data engineering, solutions architecture, machine learning operations (MLOps), user experience (UX), Agile delivery and cloud engineering. You’ll also benefit from mentoring, coaching and collaboration with experienced engineers, helping you broaden your technical knowledge ...

generation (RAG) architectures and agent frameworks such as Google ADK and LangChain, helping transform how the Bank serves customers and empowers colleagues. Data engineering, MLOps and intelligent automation , creating resilient data pipelines, scalable deployment architectures and automated machine learning workflows using contemporary engineering and CI/CD practices, enabling … machine learning AI literacy Ensemble methods & model optimisation Databases & big data Neural networks & deep learning LLM foundations, engineering,customisationand integration Generative and Agentic AI MLOps& cloud computing It’sall live lectures, tutor-led groupworkand real-world challenges from day one. Learn it. Apply it. Keep going Your personal learning plan ...

design, build, and operate the infrastructure that enterprise AI and Generative AI workloads run on: the platform layer beneath LLMs, agents, and MLOps pipelines. This spans GPU-accelerated compute and container platforms, model serving and gateway infrastructure, evaluation and guardrail systems, and the MLOps/LLMOps tooling that takes … operate core AI platform components: model serving and gateway infrastructure, agent orchestration and tool-calling frameworks, evaluation harnesses, and guardrail/governance layers• Implement MLOps and LLMOps pipelines (model deployment, monitoring, retraining, and fine-tuning where relevant) using Infrastructure-as-Code, GitOps, and CI/CD• Establish observability, security ...

development through to production. Security, scalability, maintainability, data protection, and production readiness are key priorities throughout the project. Role Purpose As a DevOps and MLOps Engineer, you will build and operate the delivery pipelines, environments, and runtime platform the project depends on, as well as the model-serving path. ...

solutions are based on sophisticated analytical thought and research. Qualifications & Skills AI/ML Architecture: AI/ML frameworks, multi‐cloud platforms, data architectures, MLOps, and secure, scalable enterprise AI solution design. Enterprise Architecture & Governance: EA frameworks, AI governance, ethical AI, compliance, and alignment to business and IT strategy. Stakeholder ...