301 to 325 of 435 MLOps Jobs in London

managing token, inference, and infrastructure spend so solutions stay cost effective as they scale. Integrate AI solutions with enterprise data systems, APIs, and MLOps/LLMOps tooling, applying sound system design and distributed-systems judgment. Apply responsible-AI judgment proportionate to the risk of each use case, working with risk … example pgvector, Pinecone, Weaviate, or Chroma) and agent or orchestration frameworks (for example LangChain, LlamaIndex, or the Model Context Protocol). Experience with MLOps/LLMOps tooling such as experiment tracking, model versioning, monitoring, evaluation/observability platforms, or CI/CD for ML and LLM systems. Experience implementing responsible ...

from discovery through to handover, with strong emphasis on architecture, integration and operational excellence. You’ll lead technical decisions, collaborate with stakeholders and drive MLOps/AIOps, DevOps and governance to ensure scalable, reliable AI solutions in live environments. #J-18808-Ljbffr ...

lead design, development, and deployment of ML/AI in Payments. You will own end-to-end delivery from framing problems to scalable MLOps and governance, mentoring engineers and data scientists, and shaping production AI strategy. You will collaborate with product, operations, risk, and technology teams to turn ideas into ...

will lead engineering teams, align with risk and data governance, and continuously evolve the platform roadmap to leverage technologies like AWS, Kubernetes, and modern MLOps #J-18808-Ljbffr ...

will be responsible for translating complex customer and behavioural data into clear, actionable recommendations. You'll establish best practices across experimentation, customer modelling, MLOps, and data science delivery, while supporting the continued growth of the organisation's customer analytics and AI capabilities. Skills and Experience Previous experience leading or managing ...

world‐class talent to solve the most complex challenges in high‐DOF autonomous systems and embodied AI. Expertise areas AI/Machine Learning MLOps Software Engineering Data Science The environment The mission is driven by high‐bandwidth, in‐person collaboration. This is a 5‐day‐a‐week, onsite role ...

create real commercial leverage Skills and Experience Proven leadership in defining and delivering enterprise AI strategy, including GenAI Strong understanding of AI platforms, MLOps, and modern AI tooling (without needing to be hands‐on) Experience translating AI opportunities into commercially impactful use cases at scale Exceptional communication, influence, and stakeholder ...

with groundbreaking businesses that are shaping the future. We operate across Cloud (DevOps, SRE, Platform Engineering, DevSecOps, Performance Engineering) and Data & AI (Machine Learning, MLOps, Data Engineering, AI Research, and related domains). Through our portfolio of innovative startups and our corporate venture arm (Harrison Clarke Ventures), we give ...

relooking for someone with extensive experience designing, building, and deploying production-grade AI andMLsolutions,supported bya strong understanding of ML engineering,Machine Learning Operations(MLOps), and model lifecyclemanagement.You’llbring the technicalexpertiseto translate analytical models and decision strategies into robust, operational systems that deliver business value within regulated environments. To succeed ...

bigger strategic programmes Solid AWS experience - S3, Lambda, EventBridge, SNS/SQS Comfortable working across TOGAF, ArchiMate, C4, UML and BPMN Hands-on with MLOps tooling and platforms - SageMaker, OpenVINO or equivalent Experience architecting data for large volumes of imagery - tiering, retention, lineage, provenance Familiar with the governance side ...

that feed our dispute systems. Collaborate with platform and backend engineers to integrate models seamlessly. Experience and qualifications 5+ years of experience as an MLOps/ML Engineer. High proficiency in writing clear, production‐ready Python code. Experience with production ML models (online or offline) and standard MLOps practices. Experience ...

solutions from development through to deployment and monitoring Building scalable Generative AI and Agentic AI solutions, including RAG pipelines and orchestration frameworks Implementing MLOps frameworks, governance and best practices Translating business requirements into cloud-native AI and data architectures across Azure, AWS and GCP Collaborating with technical and non-technical … stakeholders to deliver measurable business value Supporting innovation initiatives, reusable assets, thought leadership and client proposalsCandidates should have strong experience in AI Engineering, MLOps and cloud-native AI platforms, alongside hands-on knowledge of technologies such as LangChain, LlamaIndex, MLflow, Kubernetes, Docker and CI/CD tooling.Applicants will ideally demonstrate ...

Data Scientists and Data Engineers to operationalise machine learning solutions. Optimise model performance, reliability, and scalability across retail use cases. Support the development of MLOps processes and machine learning infrastructure. Deliver production-ready solutions that provide measurable business impact. Your Skills & Experience Strong experience deploying machine learning models into production. … Advanced Python skills and experience with machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch. Experience building and maintaining MLOps pipelines and CI/CD workflows. Strong knowledge of AWS, Azure, or GCP. Experience with Docker and Kubernetes. Strong SQL skills and experience working with large datasets. Ability ...

agentic AI approaches. Remain hands-on with development, contributing to Python code, architecture reviews, and technical problem-solving. Drive best practices across MLOps, testing, deployment, monitoring, version control, and AI governance. Partner with engineering, data, and business teams to deliver scalable, production-ready AI capabilities. Mentor and develop a team … models, generative AI, prompt engineering, and RAG architectures. Experience with Databricks, MLflow, Spark, and cloud-based AI platforms (Azure preferred). Strong understanding of MLOps, software engineering best practices, and production deployment pipelines. Experience leading technical teams while remaining actively involved in solution delivery. Excellent stakeholder management and communication skills ...

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 ...

applications on the Databricks Data Intelligence Platform. You will also continue to sharpen your technical expertise in cutting‐edge areas like GenAI, ML, MLOps, and LLMOps, while mentoring colleagues and establishing yourself as an AI thought leader. Impact you will have Architecting Workloads: Design and implement production-level … workloads, including end-to-end pipelines, training/inference optimization, MLOps lifecycle management, and integration with cloud-native services. GenAI Leadership: Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool‐calling, multi‐agent orchestration, and guardrails), AI observability, and natural language querying ...

where appropriate to accelerate insight and delivery. Provide thought leadership and best practices across the data science lifecycle, from exploration to deployment, including scalable MLOps, model governance, and responsible AI implementation. Mentor and develop data professionals, fostering a culture of innovation, continuous learning. What We're Looking ForProven experience leading … core ML libraries (e.g., spaCy, NumPy, SciPy, Transformers).Understanding of big data and cloud technologies (Spark, Hadoop, Azure, AWS, or GCP).Deep expertise in MLOps principles, scalable model deployment, model governance, and the practical application of responsible AI within enterprise environments. Excellent communication and stakeholder management skills with the ability ...

including generative AI and agentic systems, on cloud infrastructure. You will own end-to‐end delivery from problem framing and experimental design through scalable MLOps, evaluation, governance, and integration with strategic systems. You will help set technical direction, establish reusable patterns, and ensure solutions are reliable, secure, and observable … reliability, incident management, and continuous improvement Strong Python software engineering skills, including modular object‐oriented design, testing, performance tuning, and debugging Working knowledge of MLOps and LLMOps and distributed systems, including training and serving patterns, batch versus real‐time architectures, feature stores, orchestration, and scalable data processing Ability to design ...

including generative AI and agentic systems, on cloud infrastructure. You will own end-to-end delivery from problem framing and experimental design through scalable MLOps, evaluation, governance, and integration with strategic systems. You will help set technical direction, establish reusable patterns, and ensure solutions are reliable, secure, and observable … reliability, incident management, and continuous improvement Strong Python software engineering skills, including modular object-oriented design, testing, performance tuning, and debugging Working knowledge of MLOps and LLMOps and distributed systems, including training and serving patterns, batch versus real-time architectures, feature stores, orchestration, and scalable data processing Ability to design ...

managing token, inference, and infrastructure spend so solutions stay cost effective as they scale. Integrate AI solutions with enterprise data systems, APIs, and MLOps/LLMOps tooling, applying sound system design and distributed-systems judgment. Apply responsible-AI judgment proportionate to the risk of each use case, working with risk … example pgvector, Pinecone, Weaviate, or Chroma) and agent or orchestration frameworks (for example LangChain, LlamaIndex, or the Model Context Protocol). Experience with MLOps/LLMOps tooling such as experiment tracking, model versioning, monitoring, evaluation/observability platforms, or CI/CD for ML and LLM systems. Experience implementing responsible ...

Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state ...

Title: MLOps Platform Developer Location: London Salary: Depending on experience Job Type: Full time, Permanent This is a rare opportunity to be first in the door to continue the development of our engineering platform. You will own a production system end to end the web application, the database estate … with the experience and relevant job titles of: Lead Full-Stack Engineer, AI Application Engineer, Applied AI Engineer, LLM Ops Specialist, AI Platform Engineer, MLOPS Engineer, Founding Engineer, Full-Stack AI Developer, AI Software Developer may also be considered for this role. ...

workflows for researchers and ML teams. You will collaborate with researchers, ML engineers and platform teams, implement IaC, ensure security and governance, and support MLOps pipelines from experimentation to deployment in a hybrid office setting (3 days in the office). #J-18808-Ljbffr ...

involves working collaboratively to define the company's 2026 technical roadmap while solving complex challenges in autonomous systems. The role requires expertise in AI, MLOps, Software Engineering, and Data Science. The company offers a range of benefits, including family-first relocation assistance and full UK visa sponsorship. #J-18808-Ljbffr ...

InfraPlat team, building and operating the infrastructure platform that powers our massive product catalog. You’ll drive multi-account/region Kubernetes clusters, MLOps, standardized deployments and the company-wide authentication platform. Collaborating with product and engineering, you’ll scope requests, identify shared pain points, mentor teammates, and help engineers ...