1 to 25 of 118 Remote Permanent MLOps Jobs

graph-based processing, computer vision, natural language processing, and simulation modeling. Experience with generative AI and LLMs, such as LLamaIndex and LangChain Understanding of MLOps or LLMOps. Familiarity with Agile methodologies, preferably Scrum We are actively seeking candidates for full-time, remote work within the UK. More ❯

in small cross-functional squads. What we'd like to see from you: Strong understanding of a wide range of ML algorithms. Understanding of MLOps practices for managing and monitoring models in production. Experience with deep learning frameworks (e.g., TensorFlow, PyTorch). Proficiency in programming languages such as Python, SQL More ❯

with a leading technology team to build a cutting-edge AI Innovation Team, and we are looking for an exceptional Machine Learning Engineer/MLOps Engineer/AIOps Engineer/AI Data Engineer/Data Scientist/Data Science Engineer/Software Engineer Tech Lead to be at the heart More ❯

with a leading technology team to build a cutting-edge AI Innovation Team, and we are looking for an exceptional Machine Learning Engineer/MLOps Engineer/AIOps Engineer/AI Data Engineer/Data Scientist/Data Science Engineer/Software Engineer Tech Lead to be at the heart More ❯

SQL and NoSQL databases for data processing and analysis. An understanding of big data technologies, distributed computing, and real-time processing frameworks. Familiarity with MLOps practices, CI/CD, and DevOps tools. Basic understanding of electricity markets, grid operations or energy analytics. Strong problem-solving skills, the ability to work More ❯

Experience Databricks Machine Learning Associate or Machine Learning Professional Certification Developed and deployed real-time pricing, recommendation or next best action models Understanding of MLOps We are actively seeking candidates for full-time, remote work within the UK. More ❯

Familiarity with prompt engineering for model fine-tuning or inference optimization. Understanding of vector databases (e.g., Qdrant, Pinecone) and semantic search techniques. Use of MLOps tools for CI/CD pipelines in AI (e.g., MLflow, Kubeflow, SageMaker). AI for Systems Engineering Experience working with SysML, MBSE tools, or digital More ❯

IAT Level II Certification may be required (GSEC, GICSP, CND, CySA+, Security+ CE, SSCP or CCNA-Security). Advanced proficiency in Python, SQL, PySpark, MLOps, computer vision, and NLP, Databricks, AWS, Data Connections Cluster Training and tools such as GitLab, with proven success in architecting complex AI/ML pipelines. More ❯

be beneficial include: Experience in a startup environment Knowledge of Python frameworks (Django, Flask, FastAPI), Rust, and machine learning libraries Familiarity with Generative AI, MLOps, CI/CD, and cloud platforms (AWS/GCP/Azure) Expertise with container tools like Docker and Kubernetes Ready to take the next step More ❯

with stakeholders to identify and implement AI strategies that drive business value. Ensuring models are production-ready, scalable, and adhere to best DevOps/MLOps practices. Leveraging cloud environments, particularly Azure, to optimize data processing and AI model performance. Engaging with cross-functional teams, including data scientists, AI engineers, and More ❯

. Experience applying probabilistic models in real-world applications (e.g., recommendation systems, anomaly detection, personalized healthcare, etc.). Understanding of modern ML pipelines and MLOps (e.g., MLFlow, Weights & Biases). Experience with recent trends such as foundation models , causal inference , or RL with uncertainty . Track record of publishing or More ❯

Experience with debugging ML models. Experience with orchestration frameworks (e.g. Airflow, MLFlow, etc). Experience deploying machine learning models to production environments. Knowledge of MLOps practices and tools for model monitoring and maintenance. Familiarity with containerization and orchestration tools like Docker and Kubernetes. Hands-on experience with cloud platforms such More ❯

Scala, C# or Java, cloud SDKs and APIs. AI/ML expertise for pipeline efficiency, familiar with TensorFlow, PyTorch, AutoML, Python/R, and MLOps (MLflow, Kubeflow). Solid in DevOps, CI/CD automation with Bitbucket Pipelines, Azure DevOps, GitHub. Automate deployment of data pipelines and applications using Bash More ❯

of an application. Preferred Skills and Experience: Have experience integrating Machine Learning solutions into production-grade software and have an understanding of ModelOps and MLOps principles Had previous exposure to Natural Language Processing (NLP) problems and have familiarity with key tasks such as Named Entity Recognition (NER), Information Extraction, Information More ❯

Strong proficiency in Python and SQL. Hands-on experience with Power BI or similar data visualisation tools. familiarity with version control (e.g., Git) and MLOps principles. Great communication skills both verbally and written. The role is offering a salary of up to £50,000 per annum and comes with a More ❯

ideas and technology, and care deeply about collaboration, feedback, and iteration. We follow SAFe agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate-first", use modern tech stacks, and constantly challenge each other to grow and improve. If cutting edge development projects resonate with you, and More ❯

AWS Batch, Lambda, S3 Frontend: React, Next.js Key skills and experience: Experience using Python Experience with data modelling, and building data infrastructure Experienced in MLOps and building scalable data pipelines for ingestion and document processing Experience in at least one area of data science we work on: knowledge graphs, information More ❯

happen. Familiarity with ML experiment tracking and collaboration tools, such as Mlflow and Weights & Biases. A solid background in software engineering and DevOps practices, MLOps deployment, and infrastructure. A knack for generative AI frameworks and SDKs, like RAG, Langchain/LangGraph, Semantic Kernel, and tools such as MS tooling, Co More ❯

opportunities at DATAPAO, meaning that you'll get access to Databricks' public and internal courses to learn all the tricks of Distributed Data Processing, MLOps, Apache Spark, Databricks, and Cloud Migration from the best. Additionally, we'll pay for various data & cloud certifications, you'll get dedicated time for learning More ❯

We believe that we are better together, and at Tripadvisor we welcome you for who you are. Our workplace is for everyone, as is our people powered platform. At Tripadvisor, we want you to bring your unique perspective and experiences More ❯

development ✔ Strong Python, SQL, and Databricks skills (especially PySpark & Delta Lake) ✔ Deep experience with Azure: Data Factory, Synapse, Event Hub, Azure Functions ✔ Understanding of MLOps tooling like MLflow and integration with AI pipelines ✔ Exposure to data for LLM workflows, unstructured sources, and real-time ingestion ✔ Comfortable operating in an early More ❯

development ✔ Strong Python, SQL, and Databricks skills (especially PySpark & Delta Lake) ✔ Deep experience with Azure: Data Factory, Synapse, Event Hub, Azure Functions ✔ Understanding of MLOps tooling like MLflow and integration with AI pipelines ✔ Exposure to data for LLM workflows, unstructured sources, and real-time ingestion ✔ Comfortable operating in an early More ❯

our Old Street office for the rest of your time. About the Role You will design, build, and deploy production-grade software, infrastructure, and MLOps systems that leverage machine learning. The work you do will help our customers solve a broad range of high-impact problems in our Defence team More ❯

maintaining high production availability - Experience with CI/CD tools and concepts, such as Jenkins - Preferred, but not required: Experience in DevSecOps best practices, MLOps, GCP Landzone and/or DB administration Benefits Compensation: Competitive salary with the opportunity to earn on-target earnings (OTE), meaningful equity, 401(k) matching More ❯

learn. Expert-level experience in Power BI for advanced visualisations, ML model interpretation, and KPI tracking. Deep knowledge of feature engineering, model deployment, and MLOps best practices. Experience with big data processing (Spark, Hadoop) and cloud-based data science environments. Other: Ability to integrate ML workflows into large-scale data More ❯