13 of 13 Remote/Hybrid MLOps Jobs in Central London

learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: Competitive Salary & Bonus : £35,000 – £45,000 plus bonus Hybrid Working : A flexible mix of office ...

deal requirements into technical solutions. Support post‐merger integration (PMI) through AI‐enabled insights, reporting, and performance tracking. Deploy solutions into production using modern MLOps and cloud practices . Ensure models are explainable, auditable, and aligned with governance and regulatory requirements in transaction contexts. Contribute to AI strategy, architecture … Spark, Airflow or similar) LLMs, NLP, and document intelligence (e.g. RAG, embeddings) Experience with cloud platforms (AWS, Azure, or GCP). Understanding of MLOps , CI/CD, model monitoring, and governance. M&A/Transaction Experience Direct experience supporting M&A, private equity, corporate development, or transaction advisory . Proven ...

coaches, analysts, and execs Highly Desirable Skills Football Analytics Experience : Familiarity with football-specific datasets (event, tracking, positiona l), and libraries like mplsoccer Advanced MLOps & Modelling : Experience with the Vertex AI ecosystem, especially pipelines, and advanced techniques such as player valuation , tactical modelling , etc. Bayesian Modelling : Knowledge of probabilistic programming ...

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 ...

team environment Nice to Have Background in bioinformatics, computational biology, or life sciences Experience with drug discovery, protein modelling, or genomics pipelines Familiarity with MLOps, CI/CD, and scalable cloud infrastructure Exposure to graph-based models, transformers, or multimodal learning Experience in an early-stage startup or fast-scaling ...

your own on architectural trade-offs. You're fluent enough in the modern data and AI stack - Python, cloud-native services, event-driven architectures, MLOps tooling, LLM evaluation - to make informed product calls without relying on engineering to tell you what's possible. Salary and equity calibrated to the seed ...

PyTorch) and open-source contributions (especially Python-based) would be a bonus. Familiarity with CUDA, GPU-based computations, end-to-end neural network training, MLOps, and academic research in machine learning are also beneficial. At a personal level we’re also looking for someone who is: Passionate about delivering software ...

work from home. The AI Engineer will support delivery of AI use cases, workflows, analytics and solutions - responsible for tasks such as pattern recognition, MLOps, software development, neural networks, and natural language processing on a day-to-day basis. Qualifications Pattern Recognition and Neural Networks skills Computer Science and Software ...

predictive analytics — helping the world's largest employers find the right people, faster and fairer. But we're not done. Not even close. Role: MLOps Engineer Location: Remote Industry: Technology/AI & ML Infrastructure Job Function: MLOps/ML Platform Engineering Role Overview: We are hiring an MLOps Engineer ...

impact, working with diverse data types - including text, images, audio, video, and geospatial data - while applying cutting-edge methods in AI, machine learning, and MLOps/LLMOps practices. You'll have the opportunity to: Work alongside leading data scientists, ML engineers, and AI researchers on national security projects Design, develop … traditional ML use cases and GenAI/LLM applications Explore and implement ML and LLM models on complex, multi-modal datasets Apply modern MLOps/LLMOps practices, including experiment tracking, versioning, and deployment on cloud platforms Influence technical direction and best practice in ML and AI engineering Mentor and support ...

resource plans, timelines, and risk frameworks 🏗 Design AI Architectures Translate requirements into scalable AI/ML solutions Design architectures covering: Model development Data pipelines MLOps and monitoring Cloud integration (Azure, AWS, or GCP) Validate feasibility with engineering and delivery teams ✍️ Lead Tender & Bid Responses Own technical sections of AI RFPs … capability in scoping AI/ML programmes and estimating effort Deep understanding of: Machine learning (supervised & unsupervised) Generative AI & LLMs Embeddings, vector databases, RAG MLOps and model lifecycle management Experience with platforms such as: Azure ML, AWS SageMaker, Google Vertex AI Databricks, LangChain, Hugging Face Ability to develop architecture artefacts ...

Job Title Senior Data Scientist - CRM Location London – hybrid working (2/3 days in City offices) Salary To c£75,000 negotiable DoE Job Reference J13015 POSITION OVERVIEW We are super excited to be ...

adoption of large language models (e.g. Gemini) to enable intelligent automation and natural language interactions. You will also define best practices across data engineering, MLOps, and LLMOps, ensuring scalable, secure, and high-quality solutions. Working closely with Architecture, Risk, and Compliance teams, you will ensure responsible and compliant AI deployment. … native data platforms (BigQuery) Deliver batch and real-time pipelines (Dataflow, Pub/Sub, Cloud Storage) Drive AI and LLM-based automation initiatives Implement MLOps and LLMOps best practices Set standards for data quality, security, and resilience Lead and develop data and AI engineering teams Collaborate with stakeholders across ...