1 to 25 of 26 Permanent MLOps Jobs in Central London

Deep expertise in LLMs, RAG architectures, Prompt Engineering and Generative AI frameworks such as LangChain or LangGraph Hands-on experience with Python, PyTorch, TensorFlow, MLOps/LLMOps and cloud platforms (Azure, AWS or GCP) Experience deploying AI solutions within government, defence, healthcare or other high-assurance environments Strong understanding ...

applied AI Desirable Experience Hands-on experience with AWS services such as Lambda, ECS, S3, SageMaker or Bedrock Knowledge of ML engineering and MLOps practices (e.g. Docker, MLflow) Experience working in government, defence or other highly regulated environments Proven application of large language models (LLMs) to real-world problems Security ...

concepts. Excellent programming skills in Python. Demonstrated experience in building, validating, deploying, monitoring, and updating production ML and AI models. Hands-on experience with MLOps and AIOps infrastructure and tooling. Proficient in problem-solving and analytical reasoning. Exceptional communication and collaboration skills. Experience with ML frameworks such as TensorFlow, PyTorch ...

making pragmatic architectural trade-offs. Strong cloud experience, ideally within Azure. Experience deploying, monitoring and maintaining ML or LLM models in production. Familiarity with MLOps, LLMOps, evals, monitoring and lifecycle management. Hands-on experience orchestrating LLM workflows using frameworks such as LangChain. An interest in how agentic software development ...

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 ...

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 ...

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 ...

Strong experience across segmentation, propensity, lifetime value, personalisation or forecasting. Experience with large-scale behavioural, transactional or customer datasets. Understanding of production data science, MLOps and model monitoring. Commercial judgement and the ability to simplify complex technical findings. Desirable Experience providing technical leadership, peer review or coaching to other data ...

scaling ASR, TTS, and LLM pipelines across hybrid cloud and edge environments. You will implement production-grade pipelines, containerize workflows, and contribute to MLOps, AI model serving, and API development. The role requires deep expertise in Python, PyTorch, and modern ML tooling. #J-18808-Ljbffr ...

complex AI implementations. Strong communication skills and the ability to explain technical concepts clearly. Key Competencies AI and machine learning architecture Data platform architecture MLOps and model lifecycle management System integration and scalability Technical leadership Cross-functional collaboration #J-18808-Ljbffr ...

Senior ML Engineer will own the journey from experimentation and hypothesis testing through to secure, production-grade deployment, using a modern AWS-based MLOps and LLMOps platform. The Role The successful candidate will balance rapid experimentation with production readiness, prototyping and validating machine learning and generative AI approaches while ensuring …/B testing and statistical evaluation Proven experience moving models from experimentation into production with appropriate governance and quality controls Hands-on experience with MLOps tooling such as MLflow, Weights & Biases or Data Version Control Practical experience building LLM/GenAI applications, including prompt engineering and retrieval-augmented generation ...

AI. Experience developing AI-enhanced digital assistant solutions such as Microsoft Copilot. Practical experience with Retrieval-Augmented Generation (RAG) architectures and Machine Learning Operations (MLOps). Strong stakeholder engagement skills, with the ability to communicate technical solutions clearly to both technical and non-technical audiences. It would be great ...

multi cloud with strong AWS depth). Deep understanding of AI/ML solution patterns (data pipelines, training, inference, monitoring, and MLOps) and how they map onto AWS services (e.g. SageMaker, Batch, Step Functions, EKS/ECS, data lake components). Proven ability to take research and prototype environments ...

MLOps Technical Manager West London, UK (Hybrid – 2-3 days on-site)| Permanent About the Role We're recruiting on behalf of a major, well-established organisation for an MLOps Technical Manager to lead a growing ML engineering function. This is a genuinely interesting project: you'll work closely with … scalability, reliability, and alignment with business goals Drive the ongoing migration from MLflow to AWS SageMaker, maintaining continuity and minimising disruption Define and enforce MLOps best practices across model training, serving, monitoring, and deployment MLOps & Engineering Design and maintain scalable ML infrastructure supporting batch and real-time environments Oversee ...

government, and national security. Working alongside Data Scientists, Software Engineers, and AI specialists, you'll take projects from experimentation through to production, leveraging modern MLOps and LLMOps practices on AWS to build scalable, secure AI systems. What You'll Be Doing Design, develop, and deploy machine learning models for traditional … frameworks. Lead experimentation cycles, defining hypotheses, running evaluations, and iterating on models before production deployment. Develop robust ML pipelines using AWS services and MLOps tooling. Implement experiment tracking, model versioning, monitoring, and reproducibility best practices. Build feature engineering pipelines and contribute to feature store development. Monitor production models, optimise performance ...

workflows. Oversee the full technical lifecycle, from solution design and model selection through to deployment, monitoring and continuous improvement. Design and implement robust MLOps and LLMOps practices, including CI/CD, model monitoring, evaluation and governance. Deploy and optimise models using cloud-based AI services such as AWS Bedrock, Azure … LangChain, LangGraph or similar open-source AI toolkits . Experience fine-tuning, evaluating, deploying and monitoring large-scale AI systems. Strong understanding of MLOps and/or LLMOps , including CI/CD, model deployment, monitoring, evaluation and governance. Experience designing AI systems with a strong focus on security, responsible ...

environments and bare-metal local hardware. API Development: Create high-performance, asynchronous REST and WebSocket APIs using FastAPI to serve real-time conversational agents. MLOps & Infrastructure CI/CD Automation: Design automated machine learning pipelines for model testing, versioning, and continuous deployment. Containerization: Pack applications using Docker or Podman … SageMaker), Azure (Azure ML), and GCP (Vertex AI). CI/CD Tools: Experience with GitLab CI, GitHub Actions, Jenkins, or specialized MLOps platforms (e.g., Kubeflow, MLflow). Preferred Qualifications Conversational Context: Experience with dialogue management, prompt engineering, and Retrieval-Augmented Generation (RAG). Data Analytics: Familiarity with real-time ...

systems tailored to audit applications. Contribute to architectural decisions and solution governance. Write clean, efficient, and scalable code that adheres to software engineering principles, MLOps practices, and cloud-native development. AI Solution Delivery: Own the implementation of ML pipelines, APIs, and data integration workflows. Operational Excellence: Contribute to defining reusable … development patterns, enforcing coding standards, and implementing MLOps best practices that support version control, performance optimisation, and maintainability. Cross-Disciplinary Collaboration: Work side-by-side with data scientists, product managers, platform engineers, and QA teams to align on technical requirements, delivery timelines, and integration plans. Provide hands-on contributions ...

agentic workflow capabilities for legal content products. In addition to writing code on complex systems, this position provides technical direction on architecture, MLOps, responsible AI, legacy system integration, AWS-based delivery, and AI-assisted development practices. The position does not have direct reports. Key Responsibilities: Serve as the initial point … accuracy, groundedness, citation quality, hallucination risk, agent task success, latency, cost, reliability, and business value. Successfully implement development processes, coding best practices, code reviews, MLOps practices, and responsible AI controls. Apply AI-assisted development tools to reduce software development cycle time and support code explanation, test generation, refactoring, debugging, documentation ...

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 ...

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 ...

into scalable architectures and ensure integration with the client’s IT and data ecosystem. You will guide multidisciplinary teams through prototyping to production, establish MLOps practices, and uphold security, privacy, and explainability while aligning with enterprise architecture standards. #J-18808-Ljbffr ...

executives. You will bridge the gap between complex enterprise data strategies—spanning Cloud Migrations, Data Mesh architectures, Real-Time Analytics, and Generative AI/MLOps pipelines—and precise execution across globally distributed engineering pods of 100+ people. The Impact By pairing technical depth with executive gravitas, you will de-risk … Work (SOWs) and solution proposals for complex data migration, platform modernization, and AI integration initiatives that are commercially sound and technically viable. Data Quality & MLOps Governance: Establish rigorous standards for data quality, data lineage, automated testing, and MLOps/DataOps practices to guarantee model reliability and pipeline performance. Programme Governance ...

lifecycle - assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and Large Language Models), designing scalable data and DevOps & MLOps pipelines for training and production, and ensuring quality, value, and reliability of deployed systems. THE WORK Formulate real-world problems into practical, efficient, and scalable … Develop and implement machine learning algorithms, models, and computational systems design and build scalable data pipelines to support model training and production with DevOps & MLOps Customize and apply Deep Learning and Gen AI models for various use cases based on the business needs, data availability, system and infrastructure requirements - including ...

production-grade Machine Learning solutions , building scalable AI systems that solve complex real-world challenges. Design end-to-end ML architectures , including model development, MLOps pipelines, deployment strategies and continuous optimisation. Collaborate with engineering, data and product teams to deliver robust, production-ready AI solutions using modern ML and software … have taken models from development through to production. Experience with areas such as: Python and modern software engineering practices Machine Learning/Deep Learning MLOps and production ML systems Cloud platforms and scalable infrastructure ML frameworks such as PyTorch, TensorFlow or equivalent LLMs/Generative AI is highly desirable ...