101 to 125 of 409 MLOps Jobs in London

model development and deployment of AI/ML applications in a production environment, with expertise in deploying models on AWS platforms. Deep familiarity with MLOps practices, covering the full cycle from design, experimentation, deployment, to monitoring and maintenance of machine learning models. Expertise in machine learning frameworks such as TensorFlow ...

Bedrock, SageMaker, OpenAI, Cognitive Services). Strong Python programming skills with hands‐on experience using frameworks like Autogen and LangGraph . Solid grounding in MLOps , containerisation (Docker, Kubernetes), and vector databases. Understanding of agent monitoring tools (Langfuse, OpenTelemetry). Strong software engineering best practices (testing, CI/CD, code reviews ...

Create frameworks, infrastructure and systems to manage and govern Ki’s data asset Work with the broader Engineering community to develop our data and MLOps capability infrastructure Strong experience in software engineering with proficiency in a language such as Python for API development, data engineering and automation tasks A background ...

familiarity or hands-on experience with Salesforce CRM, Agentforce Studio, or Data Cloud (D360). Multi-Modal & Advanced AI Frameworks: Experience with voice pipelines, MLOps, vector search, or custom model fine-tuning. Education: BS/MS in Computer Science, Artificial Intelligence, Engineering, or equivalent technical field. #J-18808-Ljbffr ...

Familiarity with Snowflake, Databricks, Azure AI Foundry , AWS SageMaker, and other leading platforms. Data Science Expertise: Knowledge of data science fundamentals, solution lifecycles, and MLOps methodology. Generative AI Knowledge: Experience with LLMs, neural networks, and recommendation systems. Frameworks: TensorFlow, PyTorch, Hugging Face, OpenAI tools. Certifications: Cloud or AI architecture certifications ...

cognitive architectures. Solid understanding of software engineering principles, including design patterns, data structures, and algorithms. Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and MLOps practices is a plus. Excellent problem-solving skills, with the ability to translate complex business requirements into technical solutions. Strong communication and collaboration skills, with ...

OECD AI Principles, model risk management, or responsible AI frameworks. Experience with AI red teaming, model evaluation, bias testing, explainability tooling, monitoring tools, MLOps, LLMOps, or AI governance platforms. Experience advising on third-party AI tools, enterprise AI platforms, or AI-enabled vendor solutions. Certifications or advanced training ...

aligned to client objectives. Work with engineering, architecture and product teams to take prototypes through to production, monitoring and scale using appropriate cloud and MLOps practices. Lead the day‐to‐day technical delivery of a workstream, managing quality, risks and dependencies while coaching and developing team members. Communicate analytical findings ...

Build and operate ML services and APIs with authentication, role-based access and tenant isolation, so client data is never mixed across tenants. Own MLOps in production: experiment tracking, model and prompt versioning, monitoring, drift detection, cost and latency management, and incident response. Write unit, integration and evaluation tests ...

problem‐solver at heart. Nice to Haves (but we can teach you): You've already worked with Rust in a production environment. Experience with MLOps, particularly the challenges of managing the lifecycle of models in a multi‐tenant, high‐availability system. Familiarity with building ML inference services, model serialization (e.g. ...

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 ...

aligned to client objectives. Work with engineering, architecture and product teams to take prototypes through to production, monitoring and scale using appropriate cloud and MLOps practices. Lead the day-to-day technical delivery of a workstream, managing quality, risks and dependencies while coaching and developing team members. Communicate analytical findings ...

fraud, or abuse detection Proficiency in Python and experience writing production-quality code, with a solid understanding of data pipelines, model training workflows, and MLOps practices Demonstrated ability to own problems end-to-end, operate in ambiguous environments, and make pragmatic technical decisions Strong collaboration and communication skills, with ...

CreateFuture seeks a Senior Data Engineer with an ML focus to own data and feature pipelines for production ML systems. You will collaborate with data scientists and ML engineers, mentor junior engineers, and elevate engineering ...

BAE Systems Digital Intelligence in London is seeking a Senior ML Engineer to design, optimize and productionize machine learning and GenAI solutions for national security problems. You will collaborate with Data Scientists, Software Engineers, Product ...

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 ...

Performance Analysis with AI Manage the end-to-end lifecycle of trades across multi-asset classes. Develop agentic AI workflows using Python (ML) and MLOps tools to automate manual operations and reconciliations Perform daily P&L attribution and explain variance by decomposing market moves, Greeks, and new activity. Leverage ...

maintaining machine learning models in production environments Proficiency in Python and relevant AI/ML libraries (such as scikit‐learn, TensorFlow, PyTorch) Experience with MLOps practices and tools for managing the machine learning lifecycle Experience building and deploying Generative AI applications, including familiarity with LLMOps Exposure to cloud platforms (such ...

systems Strong Python skills and experience with tools such as: scikit‐learn, PyTorch, TensorFlow Pandas, NumPy Hugging Face/generative AI tooling Understanding of MLOps, cloud platforms (AWS, Azure, GCP), and SQL A passion for coaching, mentoring, and developing others Why Join Corndel? Remote, flexible, and supportive working environment Opportunity ...

production, including monitoring and troubleshooting Strong Python software engineering skills, including modular code, testing, debugging, and performance awareness Working knowledge of ML engineering/MLOps concepts, including training vs. serving, batch vs. real-time, orchestration, scalable data processing, and familiarity with model/prompt versioning and governance Ability to align ...

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 ...

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 ...

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 ...