201 to 225 of 581 MLOps Jobs in London

subject matter expert regarding GCP’s latest ML/AI tooling, and continuously prototyping and advocating for new tooling to ensure that our MLOps and AI infrastructure remains fit for purpose. Collaborating with Platform Engineering and Product to prioritise relevant workstreams across the Beatport Group. Working in conjunction with ...

retrieval systems. Familiarity with scientific, biomedical, or scholarly datasets. Experience with knowledge graphs, ontologies, or semantic enrichment systems. Exposure to production ML systems and MLOps practices. Academic or industry research experience in NLP, information retrieval, search, or generative AI. Experience working in content‐rich, knowledge‐intensive, or highly regulated domains. ...

datasets (scientific, biomedical, or enterprise data)Familiarity with scientific ontologies and metadata standards (e.g., MeSH, UMLS, ORCID, CrossRef)Exposure to production ML systems and MLOps practicesFamiliarity with data visualization and analytical tooling (e.g., Tableau, Power BI, matplotlib, seaborn, or similar) to communicate insights effectivelyExperience with human-in-the-loop evaluation ...

access expectations. Experience with PETs or privacy‐preserving ML frameworks such as Flower, Opacus, TensorFlow Federated or similar. Cloud platforms, containerisation, CI/CD, MLOps or production ML systems. Experience evaluating synthetic data using both utility and privacy metrics. Hiring Process We feel hiring should be transparent and give ...

Databricks is seeking a Senior Specialist Solutions Architect for AI/ML in London. You will be the trusted technical expert for ML/AI, guiding customers and the Field Engineering team to design production ...

WinsAbove is seeking a Senior Specialist Solutions Engineer to act as a trusted technical expert in machine learning and AI. In this hybrid role based in London, you will guide customers in implementing production-grade ...

Machine Learning, Data Science, Statistics, Mathematics or another quantitative discipline. Experience building and deploying production-grade machine learning models, with a solid understanding of MLOps, Git and CI/CD practices. Strong Python programming skills, including experience with Pandas, Scikit-learn and either PyTorch or Keras/TensorFlow. Proven experience ...

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 ...

robotics.Shape the architecture, culture and technical direction of the company from day one. What we're looking for: You'll likely come from an MLOps, AI infrastructure, platform engineering or HPC background, but titles matter less than experience. We're particularly interested in people with experience in:Large-scale distributed ...

training and optimising models using large datasets and distributed compute infrastructure.Experience working with recommendation systems, ranking, retrieval, personalisation or related machine learning domains.Knowledge of MLOps practices, including model deployment, monitoring and lifecycle management.Experience building reliable, observable and scalable services in cloud environments.Comfortable providing technical leadership and mentoring other engineers.Strong collaboration ...

Partner with Product, Architecture, and ML Research teams to prioritise and scope work. Foster a culture of modern AI development practices, including LLM tooling, MLOps, and automation. Set and enforce DevOps and SecOps standards across the team's services and pipelines. Coordinate cross‐team dependencies and contribute to roadmap planning. ...

required: workflow orchestration tools (Temporal, Airflow, Step Functions); experience building internal tools or agents; familiarity with regulated or enterprise environments; exposure to MLOps or AI evaluation frameworks. Why Planet Planet Planet is an equal opportunity employer where diversity is valued, and all employment is decided based on qualifications, merit ...

platforms Partner with business, technology, and risk stakeholders to align platform capabilities with enterprise priorities Shape platform strategy and technical roadmap, evaluating emerging AI, MLOps, and cloud technologies Ensure delivery outcomes by tracking performance metrics, managing risks, and driving adoption of platform capabilities Required Skills Strong Python engineering expertise with ...

frameworks such as TensorFlow/PyTorch. Proficiency with Python and strong software development background. Knowledge and experience with AWS is highly desirable. Experience with MLOps practices, including versioning, deployment and monitoring of models highly desirable. Ability to communicate complex technical concepts clearly to non‐technical stakeholders. Why Work for Proximie ...

with Agile software delivery at scale.Excellent communication and stakeholder-management skills, with fluent English (written and spoken).Preferred Experience and/or QualificationsFamiliarity with MLOps or LLMOps practices, and with monitoring and observing the behaviour of AI systems in production.Exposure to prompt design and prompt/response evaluation techniques ...

delivery at scale. Excellent communication and stakeholder-management skills, with fluent English (written and spoken). Preferred Experience and/or Qualifications Familiarity with MLOps or LLMOps practices, and with monitoring and observing the behaviour of AI systems in production. Exposure to prompt design and prompt/response evaluation techniques ...

Implement bounding guardrails, prompt evaluation, and anti-hallucination controls to ensure 100% compliance with MoD AI ethics, safety, and non-kinetic governance standards. Data & MLOps Pipelines: Build reproducible model training, evaluation, and containerised deployment pipelines capable of operating in air-gapped or low-bandwidth environments. Technical Client Advisory: Translate complex ...

understanding of the full ML lifecycle and strong software/platform instincts - you know what it takes to run reliable ML at scale – including MLOps tooling and practicesExperience raising operational standards: SLIs/SLOs, monitoring, incident management, and production reliabilityBudget ownership, including infrastructure cost management and vendor oversightComfortable operating ...

ranking, recommendation, or retrieval systemsFamiliarity with scientific, biomedical, or scholarly datasetsExperience with knowledge graphs, ontologies, or semantic enrichment systemsExposure to production ML systems and MLOps practicesAcademic or industry research experience in NLP, information retrieval, search, or generative AIExperience working in content-rich, knowledge-intensive, or highly regulated domainsWorking for youWe ...

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 doesn ...

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 ...

Reliability role supporting large-scale cloud systems.Experience operating GPU-backed environments or large-scale ML infrastructure.Experience running model training or inference pipelines in production (MLOps).Strong Kubernetes experience, including operating production clusters.Hands-on experience running production workloads in AWS, GCP, or Azure.Experience operating complex distributed systems in production, ideally including ...

supporting large-scale cloud systems. Experience operating GPU-backed environments or large-scale ML infrastructure. Experience running model training or inference pipelines in production (MLOps).Strong Kubernetes experience, including operating production clusters. Hands-on experience running production workloads in AWS, GCP, or Azure. Experience operating complex distributed systems in production ...

assistants. Familiarity with scientific, biomedical, or scholarly datasets and workflows. Experience with knowledge graphs, ontologies, or semantic enrichment systems. Exposure to production ML systems, MLOps, and AI governance practices. Publications or applied research contributions in NLP, IR, search, recommendation systems, or generative AI.Experience building AI systems in high-trust, regulated ...

applications and AI assistants.Familiarity with scientific, biomedical, or scholarly datasets and workflows.Experience with knowledge graphs, ontologies, or semantic enrichment systems.Exposure to production ML systems, MLOps, and AI governance practices.Publications or applied research contributions in NLP, IR, search, recommendation systems, or generative AI.Experience building AI systems in high-trust, regulated ...