5 of 5 Remote/Hybrid MLOps Jobs in Cambridgeshire

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 ...

quantitative field Cloud data platforms (Snowflake, Databricks, BigQuery, or Azure Synapse) Product analytics instrumentation experience (Amplitude, Mixpanel, Pendo, or custom event pipelines) MLOps - model deployment, monitoring, and retraining in production Experience building propensity or scoring models that integrate into CRM/CS platforms Familiarity with industrial software or enterprise SaaS ...

production ML—owning problems end-to-end to accelerate model delivery. You’ll collaborate with the ML team to improve data quality, observability, and MLOps practices, while scaling infrastructure for faster iteration and reliability. #J-18808-Ljbffr ...

several of the following areas would be advantageous: * Agentic AI * AI Agents & Autonomous Workflows * Generative AI & LLMs * Retrieval Augmented Generation (RAG) * Multi-Agent Systems * MLOps/LLMOps * LangGraph, LangChain, Semantic Kernel, CrewAI or AutoGen * OpenAI Agents, Microsoft Copilot Studio and MCP * Azure, AWS or GCP Education * 2:1 degree … Agentic AI, AI Agents, Generative AI, LLMs, Machine Learning, Data Science, Platform Engineering, LangGraph, LangChain, Semantic Kernel, CrewAI, OpenAI Agents, Microsoft Copilot Studio, MCP, MLOps, RAG, SaaS, Cambridge. ...

issues early and keep things running smoothly Troubleshooting complex issues across distributed systems, spanning data infrastructure, training, and inference Continuously improving our data and MLOps practices, and helping shape the roadmap for how our platform evolves as we scale What you'll need Strong proficiency in Python and SQL, with … quality practices (validation, cleaning, normalisation) and/or production-grade observability Ability to design resilient, scalable architectures, and comfort operating and troubleshooting distributed systems MLOps experience, for example model serving, experiment tracking, GPU/distributed training optimisation, or reproducible ML workflows A self-starter mentality: comfortable identifying problems and driving ...