176 to 200 of 336 MLOps Jobs in the UK

create measurable impact. You're comfortable working across the full AI deployment lifecycle from use case discovery and solution architecture to multimodal agent development, MLOps pipeline implementation, and production optimization. You understand what makes agents perform well in production and how to systematically improve quality through observability and evaluation. Experience … custom SDKs (Python, TypeScript) Act as trusted technical advisor to customers, shaping AI strategy and guiding roadmap decisions from concept to production Data Strategy & MLOps Infrastructure Design data architectures with efficient processing pipelines and ingestion workflows for training and inference on aion's platform Implement RAG systems with vector databases ...

scalable ML pipelines and engineering frameworks AI model training, evaluation, optimisation and deployment Cloud-native AI and data platforms across Azure, AWS and GCP MLOps tools and best practices for deployment, monitoring and governance Structured and unstructured datasets Generative AI and advanced analytics solutions Designing and maintaining scalable data pipelines … cloud environments Knowledge of data engineering principles including ETL processes, data pipelines and automated workflows Experience working with structured and unstructured data Understanding of MLOps tooling and best practices Knowledge of data governance, security and compliance principles within AI and data environments Strong analytical and problem-solving ability Experience working ...

/AI patterns, define and evangelise best practices and establish reusable approaches that enhance delivery of AI use cases across the business. Drive MLOps standards and practices across teams, including CI/CD for models, automated testing, monitoring and deployment pipelines. Collaborate with enterprise platform and data science teams, contributing … culture of continuous learning. What we’re looking for Deep expertise in Python and ML engineering. Deep expertise in ML/AI systems design, MLOps and cloud‐native architectures. Track record of leading ML/AI technical initiatives across multiple teams. Strong experience with cloud platforms (AWS, Azure, GCP). ...

integrations Fine‐tune, optimize, and deploy large language and multi‐modal models with a focus on scalability, reliability, and low‐latency performance Build scalable MLOps pipelines and cloud‐native AI applications to support secure, production‐grade deployments Collaborate with clients and cross‐functional teams to shape AI engineering strategy … enterprise or large‐scale production environments Strong software engineering expertise in Python, including backend development, API design, and distributed systems Solid understanding of scalable MLOps, observability, CI/CD practices, and cloud‐native AI deployment architectures Excellent communication, stakeholder management, and problem‐solving skills within agile delivery environments Bonus Points ...

designing and building APIs, distributed systems, and event-driven architectures in modern programming languages such as Python, TypeScript, Java, Go, or similar Familiarity with MLOps practices, model monitoring, observability, versioning, and automated deployment pipelines preferred Strong problem‐solving skills with the ability to navigate ambiguity, experiment rapidly, and deliver impactful … cost efficiency while supporting increasing AI adoption and usage Implement engineering best practices around observability, monitoring, testing, security, and operational excellence Establish and champion MLOps practices including model lifecycle management, prompt versioning, automated evaluation, and continuous improvement Build reusable frameworks, developer tooling, and platform capabilities that accelerate AI innovation across ...

UTA seeks a Principal AI Engineer to lead the design, development, and delivery of intelligent software systems that bring together enterprise application architecture, full-stack engineering, data science, and machine learning. In this highly visible ...

teams own the full delivery lifecycle, from early ideas through to production systems that create measurable impact for our customers and the business. As MLOps Engineering Manager, you will help shape how we build, deploy and operate machine learning products at scale, working closely with ML Engineers, Data Engineers, Software … Engineers, Data Scientists, Product Managers and stakeholders across Trainline. Responsibilities Build and lead a new team of MLOps Engineers, creating an environment where people can do their best work while delivering meaningful outcomes for customers and the business. Define and evolve MLOps processes, tooling and infrastructure choices across the technology ...

growth over the last few years and we’re looking to continue with this momentum as we build on our reputation as Open Source MLOps experts, expand our role within the community, and continue to deliver high-quality solutions to our clients. What we’re looking for The ideal candidate … knack for creative problem solving. As well as being a great engineer, motivated by the chance to be at the forefront of MLOps adoption, you’ll also enjoy being part of a culture that values: Loving what we do: a real passion for Open Source, MLOps, and taking pride ...

will span the full solution lifecycle—from assessing client needs to training, deploying, and monitoring models, with emphasis on scalable data pipelines, DevOps/MLOps, and collaboration with cross‐functional teams. #J-18808-Ljbffr ...

data scientists and product partners to transform innovative ideas into reliable, secure production systems. You'll design APIs, data pipelines, and deployment pipelines, applying MLOps practices, observability, and cost-efficient architectures while advancing AI capabilities across teams in a fast-paced, inclusive environment. #J-18808-Ljbffr ...

full AI engineering lifecycle, from architecture to production deployment, working with multi-disciplinary teams across client projects. The role emphasizes scalable MLOps, low-latency AI, and hands-on development in a client-facing, agile environment within Capco’s Technology Delivery team. #J-18808-Ljbffr ...

CODAS project and related initiatives at AND-E Lab. Ideal candidates have 5+ years in ML/AI, strong Python, cloud/MLOps experience, and a collaborative mindset to work with partners and stakeholders across experiments and #J-18808-Ljbffr ...

program management, or related roles Proven experience leading large-scale, cross-team projects Familiarity with the AI/ML lifecycle (model training, deployment, monitoring, MLOps) and AI/ML-based products or platforms Familiarity with Agile & Waterfall techniques (i.e PMP, PMI-ACP, Scrum) Experience in highly regulated or complex domains ...

driven systems Modern tools: Databricks, Snowflake, Kafka, dbt Cloud engineering maturity: CI/CD, Infrastructure as Code, DataOps, containers AI/ML enablement: Supporting MLOps and advanced analytics platforms Analytics expertise: BI tools and semantic data layers Certifications & frameworks: TOGAF, DAMA, cloud certifications #J-18808-Ljbffr ...

ensure solutions are secure, ethical, and fit for purpose. Working across multidisciplinary teams, you'll drive innovation in areas like DevOps, platform engineering, and MLOps - balancing experimentation with governance and risk. What you'll be doing: Leading architecture, design, and delivery of digital & AI solutions Driving innovation across AI, analytics ...

monitoring, supportability and reliability concerns in production - Experience using infrastructure-as-code tools such as Terraform - Confidence taking responsibility for live systems and outcomes MLOps experience is highly attractive, particularly operating AI or machine learning models in production. The focus is on operating models, not training them. In Return ...

negotiating with health authorities (EMA/FDA). Proven track record building and deploying AI/ML systems, including model development, validation, MLOps, and production deployment. Deep understanding of AI architecture, data pipelines, model governance, and software engineering best practices. Experience collaborating with cross-functional teams across Engineering, Data Science ...

engineering standards. Design and run large-scale distributed training on multi-node GPU clusters; drive throughput, stability, and cost efficiency in partnership with MLOps & Data Platform teams. Define what pretraining-scale data looks like: partner with the Data Collection team and external data providers to secure a steady supply ...

Senior Machine Learning Operations (MLOPS) Engineer “It feels good to have a career with real purpose.” Contract Type: Permanent Location: Alderley Park (Wilmslow) Working Style: Hybrid - 50% from home/50% office based We have an exciting opportunity for a Senior Machine Learning Operations Engineer to join Royal London ...

internal and selected external technical forums where appropriate, contributing to knowledge sharing and continuous improvement. Operations, Maintenance, Support and Documentation Leads the implementation of MLOps practices within their area, supporting efficient model deployment, monitoring and lifecycle management. Ensures that CI/CD practices are applied to AI/ML workflows … Demonstrates strong practical experience with Azure-based AI services, including Azure OpenAI, AI Search and AI Studio. Well-versed in modern machine learning approaches, MLOps practices and cloud-based AI architectures. Proficient in Python, common machine learning frameworks, distributed processing concepts and core MLOps practices. Experience designing APIs, building microservices ...

Founding Mobile Engineer Location: Chattanooga, TN ( onsite ) Salary: $120k-150k Company: the company is the modern marketplace & intelligence platform for trading cards. New grads welcome to apply. Note: The provided text contains multiple postings and ...

software code development Builds engineering stack required for Data and AIML products, including data engineering, backend engineering, Cloud infra DevOps and MLOps Designs and implements data engineering solutions, leveraging modern big data technologies Drives adoption and governance of approved AI‐assisted engineering practices across teams to improve code quality, delivery … experience developing Production‐scale Cloud‐native data engineering solutions in commercial environments Familiarity with Cloud Data engineering services (e.g., ETL, Glue, S3, Athena) and MLOps stack Demonstrated experience leading effective use of enterprise‐authorized AI‐assisted software development tools within the work environment (e.g., for coding, code review, test acceleration ...

build, testing, deployment, monitoring and handover. This is a senior, hands-on delivery role for someone who can work independently across architecture, integration, DevOps, MLOps, AIOps, data engineering and governance. You will make technical decisions, solve complex integration challenges and ensure AI solutions are reliable, scalable and safe … Landing Zone sandbox to test technical options, demonstrate value and provide clear go/no-go recommendations before full delivery. DevOps, MLOps and AIOps ownership: Implement and maintain CI/CD, monitor and release pipelines using Azure DevOps, including version control, rollback capability, automated testing and production health monitoring. Documentation ...

supporting organisations as they move from strategy and experimentation through to enterprise deployment. You will work closely with AI Engineers, Data Scientists, Enterprise Architects, MLOps teams, business stakeholders and senior leadership to design, deliver and scale AI solutions that drive measurable business outcomes. Key responsibilities: Translating business objectives into … prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience designing APIs and integrating AI solutions into enterprise environments Strong understanding of modern data architectures and platform design ...

supporting organisations as they move from strategy and experimentation through to enterprise deployment. You will work closely with AI Engineers, Data Scientists, Enterprise Architects, MLOps teams, business stakeholders and senior leadership to design, deliver and scale AI solutions that drive measurable business outcomes. Key responsibilities: Translating business objectives into … prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience designing APIs and integrating AI solutions into enterprise environments Strong understanding of modern data architectures and platform design ...