1 to 25 of 63 Remote/Hybrid MLOps Jobs in London

Snowflake, and cloud platforms Implement batch and real-time data processing with structured and unstructured data Deploy solutions on cloud with CI/CD, MLOps tools, and ensure production reliability Collaborate with business teams, ensure compliance, and mentor teams on best practices Your Skills: 7+ years in data engineering ...

deploying production-grade AI systems using Azure, Databricks and Generative AI tooling Owning the implementation of ML pipelines, APIs and data integration workflows Enforcing MLOps best practices, coding standards, version control and performance optimisation Collaborating cross-functionally with data scientists, product managers, platform engineers, QA and audit professionals Mentoring junior ...

systems Understanding of APIs, distributed systems, microservices, and system design Experience with cloud platforms such as Azure, AWS, or GCP Familiarity with DevOps/MLOps tooling including Docker, Kubernetes, CI/CD, monitoring, and logging Comfortable operating in fast-paced, product-led environments Nice to Have Experience with RAG pipelines ...

internal stakeholders.** *Excellent coding practice using Python and associated ML packages (HuggingFace, TensorFlow, PyTorch), following best practices.** *Experience developing robust ML pipelines with MLOps tools for model training, monitoring and deployment.** *Experience of appropriate version control and environment management (such as venvs or Docker).** *Experience of writing technical project ...

operational efficiency. You will be expected to operate across the full ML lifecycle, from modelling and experimentation through deployment, monitoring and continuous improvement. Strong MLOps discipline is essential, including CI/CD, automated testing, repeatable deployments, versioning, and safe release practices to keep models stable, observable and maintainable in production. ...

every model we build is fair, free from bias, and explainable to consumers, customers, and regulators. Engineering & Deployment: Work within the engineering team on MLOps to containerise, deploy, and monitor models in high-scale production. Skills & Requirements Core Data Science: Foundational Stats: You must have an excellent grasp of Linear ...

Unix environments, Git, and testing practices Understanding of data governance and security best practices Desirable Skills Experience using GPUs in cloud environments Knowledge of MLOps, CI/CD pipelines, and monitoring ML models Experience building APIs and working with modern DevOps practices Eligibility Requirements Due to the nature ...

engineering initiatives Mentor and support junior engineers within the team Contribute to engineering standards, reusable patterns, and AI best practices Help shape scalable MLOps and AI deployment approaches Drive high-quality engineering practices across testing, deployment, monitoring, and optimisation Tech environment Key technologies include: Python Azure Databricks Generative AI/ ...

recommendation 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 Publications or applied research contributions in NLP, IR, search, or generative AI Experience building AI systems in regulated, high-trust, or content-rich domains ...

PyTorch, or Hugging Face Solid understanding of machine learning fundamentals and performance evaluation techniques Experience working in cloud platforms (AWS, GCP, or Azure) with MLOps tools (e.g. MLflow, SageMaker, Vertex AI) Comfortable working independently and delivering high-quality work to tight timelines Experience working in fast-paced environments or scale ...

analytics alongside AI solutions). Technical Scope Platform-agnostic engineering mindset; able to work across varied client stacks. Experience with cloud AI services and MLOps/LLMOps (e.g., Azure AI Foundry, AWS SageMaker and/or Bedrock). Strong software engineering practices: Python, APIs, containerisation, CI/CD, IaC, testing ...

GenAI solutions, from data preparation through to production deployment. Designing and building robust ML pipelines, CI/CD processes and MLOps frameworks. Leading GenAI initiatives such as document processing, LLM fine-tuning, prompt engineering and orchestration using modern frameworks. Organising and processing large, complex datasets to enable ...

personalization Forecasting and optimization Ensure models are scalable, explainable, and production-ready Work closely with engineering teams to operationalize models within production environments (MLOps) Product & Innovation Partner with Product, Engineering, and Architecture teams to embed AI capabilities into SBS solutions Identify opportunities to leverage AI/ML to enhance banking ...

curated data layers. Establish and enforce standards for coding, CI/CD, testing, and deployment across all data delivery teams. Ensure consistent implementation of MLOps & MLflow for model lifecycle management and reproducibility. Establish & Lead Data Solution Architecture practice to ensure design integrity and proper execution of data architecture patterns. Lead ...

Competencies Exposure to AI governance, security, or compliance considerations. Experience deploying ML models in cloud environments (Azure preferred, AWS acceptable). Familiarity with MLOps practices (model versioning, evaluation, monitoring, iteration). Experience working with non‐ideal, real‐world data rather than curated research datasets. Experience working in both enterprise ...

track record of developing and deploying models at scale Expertise in Python, ML frameworks (TensorFlow, PyTorch), and experience with cloud platforms Strong familiarity with MLOps principles, CI/CD pipelines, and containerization (Docker, Kubernetes) With preferred backgrounds covering some of the following: Experience with speech‐to‐text or text ...

LLMs and RAG architectures. Python: Strong proficiency alongside PySpark for data engineering. Azure: Deep familiarity with native tools (AI Search, Document Intelligence). MLOps: Understanding of LLMOps frameworks for production deployment. 6 Months Remote Contract | £600 a day inside IR35 | Active Security Clearance Are you a seasoned technical leader with ...

LLMs and RAG architectures. Python: Strong proficiency alongside PySpark for data engineering. Azure: Deep familiarity with native tools (AI Search, Document Intelligence). MLOps: Understanding of LLMOps frameworks for production deployment. 6 Months Remote Contract | £600 a day inside IR35 | Active Security Clearance Are you a seasoned technical leader with ...

associated architecture patterns Experience designing and managing APIs including REST and GraphQL Strong understanding of microservices and serverless architectures Practical experience with DevOps and MLOps practices including CI/CD pipelines Knowledge of security frameworks, identity management and authentication standards Desirable Skills for the AI Solutions Architect: Experience with enterprise ...

related fields. Strong knowledge of AI, machine learning, deep learning, and probabilistic modeling. Proficient in Python, PySpark, data modeling, and microservices. Experience in MLOps, AI lifecycle management, and LLM orchestration on cloud platforms (AWS, GCP, Azure). Familiar with DevOps tools (Azure DevOps, GitHub Actions, Jenkins, Terraform, AWS CloudFormation). ...

Adaptive Decision Manager in production environments is highly desirable. * Exposure to real-time decisioning, customer engagement, or personalisation use cases is advantageous. * Familiarity with MLOps practices and model monitoring frameworks is beneficial. How to Apply If you are looking for a senior data science role where you can combine technical ...

stakeholders to translate business requirements into actionable technical solutions , manage delivery risks, and prioritise backlogs. Establish and enforce engineering best practices , including DevOps/MLOps, coding standards, testing frameworks, and documentation. Design and deliver innovative, scalable, secure, and sustainable solutions aligned with industry standards. Facilitate workshops and stakeholder engagement sessions ...

Experience with agent frameworks, tool-use planning and workflow orchestration Systems-level fluency across data pipelines, training infrastructure and inference (PyTorch, JAX, TensorFlow) Strong MLOps discipline: CI/CD for models, offline/online evaluation, safety and guardrails The ability to influence at exec level and mentor senior technical talent ...

Responsibilities: Research state-of-the-art machine learning algorithms to solve network orchestration problems. Develop machine learning training infrastructure using kubernetes clusters, and managing MLOps tooling. Develop and maintain documentation related to novel algorithms developed by the team. Integrate AI technology with other components of the Spacetime platform to ensure ...

MLOps Platform Manager, Palantir Fully Remote | 6 Month Contract | Outside IR35 | £550 to £650 per day Overview A leading organisation is looking for an MLOps Platform Manager to take ownership of its Palantir and production ML platform environment, covering Foundry, AIP and wider MLOps services . The role will focus … products into production in a structured and repeatable way. The Role This is a delivery-focused platform ownership role, sitting across: Palantir platform operations MLOps enablement Service management Security, governance and compliance Supplier and vendor management You will be responsible for the day-to-day running and improvement of Palantir ...