251 to 275 of 371 MLOps Jobs in England

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 ❯

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 vector search architectures Ability to build autonomous agents that interact with APIs, fetch public data and trigger external actions Experience deploying AI solutions on Azure Strong understanding of MLOps, data engineering and model lifecycle management Experience embedding AI features into a SaaS or operational technology platform Experience working in the manufacturing industry or with manufacturing, supply chain or inventory 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 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 ❯

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 ❯

Data Science Engineer - MLOPS, Machine Learning, AI, Artificial Intelligence, Azure, PyTorch, TensorFlow, LangChain, OpenAI, Docker, Kubernetes, GenAI, ETL We are actively working with a global law firm who are actively looking to bolster their IT team as they undergo a global-scale cloud transformation. At present they are looking to take on a new Data Science Engineer (MLOPS, Machine Learning … tier global law firm who have a long-stream of projects in the pipeline alongside a diverse and collaborative team environment. To be considered for this Data Science Engineer (MLOPS, Machine Learning, AI, Artificial Intelligence, Azure, PyTorch, TensorFlow, LangChain, OpenAI, Docker, Kubernetes, GenAI, ETL) role, it's ideal you have: Ideal but not required law firm experience 2-4 years … science and AI solutions end-to-end, from design and development through testing, release, monitoring, and support. Operationalize models with CI/CD pipelines, automated testing, and monitoring, applying MLOps practices such as versioning, retraining, and drift detection (tools: MLflow, Azure ML, Databricks) Leverage both open-source frameworks (LangChain, Hugging Face, etc.) and enterprise platforms (Azure OpenAI, Databricks, etc.) to More ❯

large (AI) transformational journeys BCG does for its clients. Often involves the following engineering disciplines : Cloud Engineering Data Engineering (not building pipelines but designing and building the framework) DevOps MLOps/LLMOps Often work with the following technologies : Azure, AWS, GCP Airflow, dbt, Databricks, Snowflake, etc. GitHub, Azure DevOps and related developer tooling and CI/CD platforms, Terraform or … other Infra-as-Code MLflow, AzureML or similar for MLOps; LangSmith, Langfuse and similar for LLMOps The difference to our "AI Engineer" role is: Do you "use/consume" these technologies, or are you the one that "provides" them to the rest of the organization. What You'll Bring TECHNOLOGIES: Programming Languages: Python Experience with additional programming languages is a 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 ❯

/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 ❯

MLOps Engineer - Forecasting/Cloud Remote - UK (O/IR35), NL, BE, GER 3 - 6 Months initial contact Join an innovative technology company modernising its data science and AI capabilities. You’ll take ownership of how machine learning models are built, deployed, and scaled across distributed cloud environments — helping the business embed modern AI best practices and robust MLOps pipelines. … algorithms that improve decision-making across the energy domain. This includes defining and setting up the end-to-end ML infrastructure, mentoring engineers, and shaping how the organisation approaches MLOps and AI enablement. What You’ll Do • Build and maintain ML pipelines and CI/CD processes for model training, validation, and deployment. • Lead the implementation of forecasting models and … environments. • Work with engineering and product teams to embed ML capabilities into production systems. • Optimise performance using tools such as AWS, Databricks, and containerisation frameworks. • Define best practices for MLOps, monitoring, and version control. • Provide technical guidance and education to teams adopting AI tooling. What You’ll Bring • 5+ years in software/ML engineering, ideally with production deployment experience. More ❯

which we work and live. It is personal to all of us.” – Julie Sweet, Accenture CEO. We are a dynamic team of specialists in Data Science, Data Engineering, and MLOps, dedicated to delivering cutting-edge AI solutions within secure government and defence environments. Join us to be part of an inclusive and collaborative environment where innovation thrives, and continuous learning 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 ❯

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 ❯

Scientists, Data Engineers, and DevSecOps teams , building automation pipelines that accelerate model development and deployment across distributed, cloud-native systems. Key Responsibilities Design, implement, and maintain end-to-end MLOps pipelines for model training, validation, deployment, and monitoring. Develop and automate workflows using Terraform, Kubernetes, Docker , and CI/CD toolchains (GitHub Actions, Jenkins, Argo, etc.). Manage scalable cloud … pipelines. Contribute to the development of a modular, reusable ML platform architecture supporting multi-modal data (genomic, clinical, imaging, etc.). Essential Skills and Experience Proven experience as an MLOps Engineer, Platform Engineer, or DevOps Engineer supporting ML or data science teams. Strong hands-on experience with containerization (Docker) and orchestration (Kubernetes) . Expertise in Terraform , Infrastructure as Code (IaC … Solid understanding of CI/CD pipelines and automated testing frameworks. Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. Familiarity with MLflow, Kubeflow, DVC, or similar MLOps tools . Understanding of cloud security principles , IAM, and networking best practices. Proficiency in Python and Bash scripting for automation and tooling development. Version control with Git , and collaborative development 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 ❯

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 … analytics TO BE CONSIDERED Please apply directly by emailing 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 ❯

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 ❯

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 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 ❯

CI/CD pipelines for data workflows and ensure lineage, observability, and compliance across environments. Collaborate with AI/ML teams to support model training, deployment, and monitoring using MLOps frameworks. Establish and enforce data governance policies, stewardship models, and metadata management practices Monitor and improve data quality using rule-based profiling, anomaly detection, and GenAI-powered automation Support GenAI … experience Data Quality:Ability to implement profiling, cleansing, standardization, and anomaly detection frameworks. Security & Compliance:Knowledge of data privacy, access control, and secure data exchange protocols. Defining and creating MLOPs pipeline Good to Have Skills GenAI Exposure:Experience with LLMs, LangChain, HuggingFace, synthetic data generation, and prompt engineering. Digital Twin Integration:Familiarity with nVidia Omniverse, AWS TwinMaker, Azure Digital Twin More ❯

will help managethe ML lifecycle fromdata selection and collection, ML model design and creation all the way through tooperationalizationand monitoring. You will work closely with data scientists and senior MLOps Engineers to understand and implement models into production. At Data Reply, you'll enjoy extensive training opportunities coupled with a detailed learning pathto guide you along the way.You'll thrive … learning models to production Implement solutions to monitorthe performance of Machine Learning models in production over time Work in teams with other technical experts, e.g. Data Engineers, Data scientists, MLOps Engineers, Data Visualization Specialists Interact with domain experts from different industries to understand and tackle challenging problems Explore and understand client datain relation to the problem you're tackling andcommunicate More ❯