1 to 25 of 387 Remote 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 ❯

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. #J-18808-Ljbffr 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 ❯

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 ❯

science projects. Strong communication and presentation skills. Knowledge of Deep Learning, NLP, Large Language Models, and Generative AI. Experience with cloud environments, Dev/MLOps, Docker, Kubernetes, and Agile methodologies. Experience in the financial industry or large organizations is preferred. Connect to your business - Audit & Assurance Our focus is on 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 ❯

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 ❯

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 ❯

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 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 ❯

XGBoost, LSTMs, transformers), and statistical modeling. Proficient in Python and machine learning libraries such as Scikit-learn, TensorFlow, PyTorch, etc. Hands-on experience with MLOps tools and practices: model tracking, drift detection, continuous learning. Strong background in data processing, feature engineering, and scalable ML pipelines. Experience working in cloud environments 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 ❯

At Goodnotes, we believe that every individual holds untapped potential waiting to be unleashed. By reimagining the way we interact with information, we’re merging human creativity with the breakthrough capabilities of AI. Our renewed vision and mission drive us More ❯

define AI requirements and deliver impactful solutions. Build and deploy AI models using industry-leading frameworks and cloud platforms (Azure, AWS, GCP). Apply MLOps principles and software engineering best practices to develop scalable AI applications. Work alongside cross-functional teams to integrate AI solutions into business operations. What You More ❯

define AI requirements and deliver impactful solutions. Build and deploy AI models using industry-leading frameworks and cloud platforms (Azure, AWS, GCP). Apply MLOps principles and software engineering best practices to develop scalable AI applications. Work alongside cross-functional teams to integrate AI solutions into business operations. What You 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 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 ❯

at least one of the major cloud providers (AWS/GCP/Azure). During your work operationalizing data science solutions, you have implemented MLOps best practices and understand their value in building sustainable solutions. You have successfully guided data science teams and can demonstrate a track record of mentoring More ❯

development (AWS/Azure). Preferred skills & experience: Experience integrating Machine Learning solutions into production-grade software with a sound understanding of ModelOps and MLOps principles and the ability to translate between language and methodologies used both in research and engineering fields. Had previous exposure to Natural Language Processing (NLP More ❯

Qualifications: Strong experience in AI/ML development focusing on LLMs and biomedical NLP. Proficiency in deep learning frameworks (TensorFlow, PyTorch). Experience with MLOps tools and cloud environments (AWS, Azure, GCP). Expertise in model inference optimization techniques. Understanding of biomedical ontologies and healthcare data formats. Experience deploying AI 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 ❯

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 ❯

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 ❯