176 to 200 of 603 MLOps Jobs in England

object localisation or multi-modal perception systems. GPU optimisation, CUDA, inference acceleration or edge AI. Real-time computer vision systems. Docker, Kubernetes, Kubeflow or MLOps pipelines. Aerial, satellite or ISR imagery. Synthetic data generation, Unreal Engine or NVIDIA Omniverse. Distributed training and large-scale model optimisation. Defence, government or national ...

including serving, monitoring, and retraining Experience leading or mentoring engineering squads Hands‐on experience building and training deep learning models in PyTorch Familiarity with MLOps tooling such as MLflow and CI/CD workflows Experience with AWS, Azure, or GCP Experience with sequence models (e.g. transformers, RNNs) or generative models ...

including serving, monitoring, and retraining Experience leading or mentoring engineering squads Hands‐on experience building and training deep learning models in PyTorch Familiarity with MLOps tooling such as MLflow and CI/CD workflows Experience with AWS, Azure, or GCP Experience with sequence models (e.g. transformers, RNNs) or generative models ...

related technical discipline. Minimum 2 years of experience in Generative AI, Large Language Models (LLMs), machine learning, AI platforms, cloud computing, software engineering, MLOps, solution architecture, or customer‐facing technical consulting. Hands‐on experience with modern AI technologies including LLMs, Prompt Engineering, RAG, AI Agents, MCP, model fine‐tuning, inference ...

/MATLAB, with proficiency in ML libraries Strong knowledge of Machine Learning and AI techniques Experience with deploying operational signal processing systems Familiarity with MLOps and cloud computing platforms; Azure preferred Excellent communication and teaching skills, able to explain complex technical concepts to diverse audiences Passion for continuous learning ...

Snowpark, AWS Bedrock, SageMaker, vector search and semantic search architectures Experience delivering Business Intelligence platforms, self-service analytics solutions and working knowledge of DataOps, MLOps and LLMOps practices Demonstrable experience working in telecommunications and using industry best practice frameworks such as TM Forum, TOGAF, ITIL, BPMN and UML Life ...

complex professional environments. Strong consulting, stakeholder engagement, problem‐solving, and technical leadership capabilities. Desirable AWS Tools & Technologies experience: Amazon SageMaker - ML development, training, deployment & MLOps Amazon S3 - Scalable data storage & data lakes AWS Glue - Data integration, ETL & preparation Amazon Redshift & Athena - Data warehousing & serverless analytics Amazon EMR - Big data processing ...

related technical discipline. Minimum 2 years of experience in Generative AI, Large Language Models (LLMs), machine learning, AI platforms, cloud computing, software engineering, MLOps, solution architecture, or customer-facing technical consulting. Hands-on experience with modern AI technologies including LLMs, Prompt Engineering, RAG, AI Agents, MCP, model fine-tuning, inference ...

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 Ultralytics ...

ability to help refine problem statements and figure out how to solve them Some extra skills that are great (but not essential): Experience with MLOps tools: Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR, CI/CD Prior experience in the fraud domain and a strong understanding of fraud detection ...

senior stakeholders Excellent Python skills using Scikit-learn and PyTorch, experience working in VS Code with AI-assisted coding tools, and knowledge of MLOps for production deployment Familiarity with generative AI, AI agents and agentic workflows, and an interest in applying them to real business problems Benefits and perks There ...

Scalable Pipelines: Partner with data engineers and global IT teams to ensure AI solutions are scalable, secure, and integrated with CI/CD and MLOps standards. Bridge Tech and Strategy: Translate complex algorithmic outputs and statistical concepts into clear, high-impact business strategies for senior stakeholders. Advance Technical Excellence: Stay ...

several days per week. Preferred/Nice to Have Background in high‐availability, low‐latency, or highly regulated environments (e.g. fintech, trading, healthcare). MLOps experience — deploying, monitoring, and scaling machine learning workflows in production. Experience with ClickHouse, Amazon Redshift, or other data warehousing/analytics platforms. Strong identity ...

event-driven architectures.* Experience with Infrastructure as Code (e.g. CloudFormation, CDK, Terraform).* Any experience with MCP (Model Context Protocol) and/or MLOps (e.g. model deployment pipelines, monitoring and lifecycle management) is highly desirable.* Understanding of compliance requirement around SDLC, ITGC, ITAC.* Experience with Flyway or similar database migration ...

practices — explainability, bias review, model auditability Experience with insurance platforms (Duck Creek, Guidewire, Acturis) Experience with pricing or actuarial model deployment Familiarity with MLOps concepts (CI/CD pipelines, model registries, automated testing) — awareness required, not hands‐on ownership Experience with ML lifecycle tooling — MLflow, Azure ML, or equivalent Python ...

experimentation, deployment, monitoring, drift, and iteration Experience partnering with engineering and data science on technical architecture, APIs, data pipelines, workflow orchestration, scalable data products, MLOps, observability, and reliability Experience defining evaluation and validation frameworks for AI or ML products, including offline metrics, online experimentation, human evaluation, quality thresholds, error analysis ...

scale, vector stores, agentic orchestration, multimodal work, evaluation infrastructure and fine-tuning where it earns its place. You're fluent in the layer underneath: MLOps tooling, deployment and cloud infrastructure (we run on AWS and GCP), and you can tell the difference between a choice that matters and one that ...

Trips vertical. Talent Multiplier: Mentor and coach senior and mid‐level ML scientists. Foster a culture of technical excellence, driving best practices for MLOps, rigorous A/B testing, data privacy, and code quality. Skills & Experience: Education: Ph.D. or Master’s degree in Computer Science, Machine Learning, Statistics ...

track record of developing and deploying models at scale Expertise in Python, ML frameworks (TensorFlow, PyTorch), and experience with cloud platforms Strong familiarity with MLOps principles, CI/CD pipelines, and containerization (Docker, Kubernetes) With preferred backgrounds covering some of the following: Experience with speech-to-text or text ...

Trips vertical. Talent Multiplier: Mentor and coach senior and mid-level ML scientists. Foster a culture of technical excellence, driving best practices for MLOps, rigorous A/B testing, data privacy, and code quality. Skills & Experience: Education: Ph.D. or Master’s degree in Computer Science, Machine Learning, Statistics ...

Ubuntu Pro Services - Our key commercial offerings Ubuntu Pro, Compliance, Standards, Security Engineering, and Managed Services on cloud and on prem AI/ML & MLOps - Open source AI/ML solutions, AIOps automation, model lifecycle management, Kubeflow, MLFlow, KServe, and AI infrastructure on cloud and edge IoT - Ubuntu on embedded ...

Deloitte - Recruitment in London is seeking a Software AI Engineer for a contract role. You will work on AI-driven financial services platforms, building intelligent agents, copilots and ML-powered solutions across software engineering, Generative ...

Ki Insurance is building a cutting-edge data platform to monitor the commercial performance of its algorithmically underwritten insurance business in real time. You will join an agile squad, collaborating with actuaries, data scientists and ...

Generation (RAG), Agent Development Kits (ADK), Model Context Protocol (MCP), and Google Cloud Platform (GCP). The role provides exposure to cloud-native architecture, MLOps, AI governance, and enterprise-scale solution delivery, enabling you to contribute to transformational programmes while remaining at the forefront of AI innovation. Key Responsibilities Design … engineering teams to translate use cases into technical solutions. Deploy and manage AI workloads on GCP using modern cloud-native and container technologies. Implement MLOps best practices covering model deployment, monitoring, CI/CD, automation, and governance. Ensure solutions meet enterprise standards for security, reliability, scalability, and performance. Conduct performance ...

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/ ...