1 to 25 of 209 Permanent MLOps Jobs

and working experience of AGILE methodologies. Proficient with SQL. Familiarity with Databricks, Spark, geospatial data/modelling and insurance are a plus. Exposure to MLOps, model monitoring principles, CI/CD and associated tech, e.g., Docker, MLflow, k8s, FastAPI etc are desirable We’ll help you gain Experience working in more »

Physics, Machine Learning Deep expertise in Python (production-level) and SQL Proficiency in machine learning libraries (eg. Pandas, scikit-learn, TensorFlow) and experience with MLOps frameworks for model deployment Exceptional communication skills, able to engage confidently with non-technical stakeholders Experience resolving operational or customer experience-related problems such as more »

Statistics, Mathematics, Physics, Machine LearningDeep expertise in Python (production-level) and SQLProficiency in machine learning libraries (eg. Pandas, scikit-learn, TensorFlow) and experience with MLOps frameworks for model deploymentExceptional communication skills, able to engage confidently with non-technical stakeholdersExperience resolving operational or customer experience-related problems such as workforce management more »

computational resources, inference speed, and memory efficiency. Good grasp of architecture and how best to integrate machine learning into backend services. Some experience with MLOps processes and tools. Experience with AWS SageMaker would be ideal. Understanding of CI/CD & DevOps principles. Experience working with cloud environments. If you feel more »

the agile framework and processes enabling every function to turn requirements and ideas into insights that support data based decisions. Adopt DevOps, DataOps and MLOps frameworks and best practices as the standard for all data engineering across the company. Development and monitoring of MI and AI engineering quality metrics and more »

for people that are comfortable with terms like; Statistical Models, Computer Vision, Predictive Analytics, Data Visualization, Large Language Models (LLM), NLP, AI, Machine Learning, MLOPs, Python, Pyspark, and Azure. Flexible Working : The role is 60% work from home Sponsorship : Sadly sponsorship isn't available for these roles. About You: Bachelors more »

Pandas, scikit-learn, R (tidyverse, glm, caret etc ), MLFlow, Experimentation, Tracking, Productionizing and proficient in SQL. Three or more years of professional experience in MLOps, Data Engineering, software engineering, or a related field. Infrastructure Operations: Minimum 3 to 7+ years of hands-on experience in some combination of the following more »

cloud SDKs and APIs. Deep understanding of AI/ML to enhance pipeline efficiency, with experience in TensorFlow, PyTorch, AutoML, Python/R, and MLOps platforms like MLflow and Kubeflow. Solid background in DevOps , including CI/CD automation with Bitbucket Pipelines, Azure DevOps, and GitHub. Capability to automate the more »

unsure or only feel that your experience aligns with only some of the requirements above, we'd still encourage you to apply) DevOps/MLOps (CI/CD, Release Management, Kubernetes, Docker) Programming skills, with experience of writing clean, well-documented, and unit-tested python ETL processes to read, transform more »

unsure or only feel that your experience aligns with only some of the requirements above, we'd still encourage you to apply) DevOps/MLOps (CI/CD, Release Management, Kubernetes, Docker) Programming skills, with experience of writing clean, well-documented, and unit-tested python ETL processes to read, transform more »

NoSQL databases (eg, MongoDB, Cassandra). Familiarity with DevOps practices and tools (eg, Jenkins, Docker, Kubernetes) for efficient deployment of AI solutions. Understanding of MLOps principles to streamline the machine learning life cycle from experimentation to production. Responsibilities Lead and expand the strategic vision for deploying AI and advanced analytics more »

functional teams and departments. Willingness to learn new technologies and adapt to evolving industry trends. Desirable Knowledge of Azure ecosystem Experience in working with MLOps frameworks (preferably Azure-ML) Experience with CI/CD pipelines, Test-Driven Development and pipeline automation. more »

MLOps. Responsibilities Design and implement both Data Pipelines for both structured and unstructured data. Support and improve ML infrastructure – particularly Azure ML. Contribute to MLOps across the organisation. Requirements An academic background in Statistics, Mathematics, or Computer Science (or similar) A number of years of commercial work as a Senior more »

MLOps. Responsibilities Design and implement both Data Pipelines for both structured and unstructured data. Support and improve ML infrastructure – particularly Azure ML. Contribute to MLOps across the organisation. Requirements An academic background in Statistics, Mathematics, or Computer Science (or similar) A number of years of commercial work as a Senior more »

MLOps. Responsibilities Design and implement both Data Pipelines for both structured and unstructured data. Support and improve ML infrastructure – particularly Azure ML. Contribute to MLOps across the organisation. Requirements An academic background in Statistics, Mathematics, or Computer Science (or similar) A number of years of commercial work as a Senior more »

services. Experience with database management and querying tools, including traditional SQL databases (eg, MySQL, PostgreSQL) and NoSQL databases (eg, MongoDB, Cassandra). Understanding of MLOps principles to streamline the machine learning life cycle from experimentation to production. Exposure to legal-tech software and platforms is beneficial, as it provides contextual more »

services. Experience with database management and querying tools, including traditional SQL databases (eg, MySQL, PostgreSQL) and NoSQL databases (eg, MongoDB, Cassandra). Understanding of MLOps principles to streamline the machine learning life cycle from experimentation to production. Exposure to legal-tech software and platforms is beneficial, as it provides contextual more »

frameworks (Dask, Modin) Hands on experience with Natural Language Processing (NLP) and Large Language Models (LLMs) Experience of creating and deploying microservices Knowledge of MLOps concepts (CI/CD, versioning, reproducibility, observability) and development best practices ABOUT US J.P. Morgan is a global leader in financial services, providing strategic advice more »

and libraries. Experience with ML experiment tracking and collaboration tools like MLflow and Weights & Biases . A strong foundation in software engineering, DevOps practices, MLOps deployment, and infrastructure management. An affinity for generative AI frameworks and SDKs , such as RAG, Langchain, Semantic Kernel, and tools like MS tooling, Co-Pilot more »

environments, ensuring seamless integration with legacy systems. S trong AI and ML principles , with a track record of model development, deployment, and mastery of MLOps practices. Experienced in crafting enterprise-scale cloud-native AI/ML solutions Proficiency in data pipeline orchestration across hybrid environments, leveraging the latest in Azure more »

Job DescriptionTransform the World of Content Creation as an AI Engineer.We are excited to offer an opportunity for a highly skilled AI Engineer to enhance a revolutionary Content AI platform. Based in the vibrant heart of tech innovation, this team more »

Transform the World of Content Creation as an AI Engineer on a 12-month FTC We are excited to offer an opportunity for a highly skilled AI Engineer to enhance a revolutionary Content AI platform. Based in the vibrant heart more »

Scientist in the Financial Services with experience in..... Statistical Models, Computer Vision, Predictive Analytics, Data Visualization, Large Language Models (LLM), NLP, AI, Machine Learning, MLOPs Python, Pyspark, Azure, Agile, MetaBase, then please apply. You can 📧 your cv to matt@hawksworthuk.com or message me on LinkedIn. Ideally you'll have plenty more »

datasets, experience with Azure Data Factory, Azure SQL Database, and Cosmos DB. Understanding of CI/CD pipelines, containerization (Docker, Kubernetes), and experience implementing MLOps practices using Azure DevOps. Strong analytical and problem-solving abilities, with the capability to work on complex issues and drive innovative solutions. Deep understanding of more »

Data Engineers, as well as Cloud Software Engineers and DevOps in a Scrum environment. You can support streamlining deployment and establish best practices in MLOps for large models to build a better future where a new job is just a click away. Your responsibilities: Implement and productionise ML-based services more »