to haves includes: Kubernetes, Docker, BigQuery, Cloud, Databases Interest in FinTech Data Engineer Responsibilities: Creating new data pipelines Implementing automated data pipelines and overseeing MLOps processes to streamline the workflow of the Data Scientists Examine code from fellow engineers, contribute to the establishment of best practices, and design patterns for more »
methods Ensure that all ML-based infrastructure is in the best possible state. Requirements Commercial experience working as an ML Engineer model deployment) or MLOps/Data Science Engineer role. End to end "full stack" ML experience in AWS Strong Python coding skills CI/CD experience Ideally some Time more »
environments. Hands-on experience with cloud-based AI and ML solutions (e.g. Azure). Knowledge of generative AI applications and embedding models. Proficiency in MLOps and DevOps practices. Strong programming skills in Python and C#. Experience with Big Data technologies and ETL processes. Familiarity with distributed computing frameworks like Kubernetes. more »
deployment. Solid understanding of SDLC and CI/CD processes. Desired : experience in Infrastructure as Code developments and tools e.g. Terraform Desired : experience with MLOps deployment and maintenance. Desired: Data Engineering technologies e.g. ETL , Spark , Dataflow , BigQuery Please note: even if you don't have exactly the background indicated, do more »
Management: Experience with database management and querying tools, including traditional SQL databases (eg, MySQL, PostgreSQL) and NoSQL databases (eg, MongoDB, Cassandra). DevOps and MLOps: 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 more »
Management: Experience with database management and querying tools, including traditional SQL databases (eg, MySQL, PostgreSQL) and NoSQL databases (eg, MongoDB, Cassandra). DevOps and MLOps: 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 more »
Management: Experience with database management and querying tools, including traditional SQL databases (eg, MySQL, PostgreSQL) and NoSQL databases (eg, MongoDB, Cassandra). DevOps and MLOps: 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 more »
Management: Experience with database management and querying tools, including traditional SQL databases (eg, MySQL, PostgreSQL) and NoSQL databases (eg, MongoDB, Cassandra). DevOps and MLOps: 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 more »
Management: Experience with database management and querying tools, including traditional SQL databases (eg, MySQL, PostgreSQL) and NoSQL databases (eg, MongoDB, Cassandra). DevOps and MLOps: 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 more »
Management: Experience with database management and querying tools, including traditional SQL databases (eg, MySQL, PostgreSQL) and NoSQL databases (eg, MongoDB, Cassandra). DevOps and MLOps: 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 more »
Management: Experience with database management and querying tools, including traditional SQL databases (eg, MySQL, PostgreSQL) and NoSQL databases (eg, MongoDB, Cassandra). DevOps and MLOps: 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 more »
Management: Experience with database management and querying tools, including traditional SQL databases (eg, MySQL, PostgreSQL) and NoSQL databases (eg, MongoDB, Cassandra). DevOps and MLOps: 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 more »
MLOps Engineer Up to 100k Hybrid - London 2/3 days a week My Client is a global luxury retailer looking for an MLOps Engineer to join their strong AI department, where you will help build and maintain the ML infrastructure as well as supporting the deployment and productionisation of … reliability of the ML systems. Apply software engineering principles Work cross functionally across the business What you need to succeed: Strong background as a MLOps Engineer, Platform Engineer or Devops Engineer Must have experience working on ML infrastructure using Docker and Kubernetes Minimum of 3 to 5 years experience Extensive more »
deployment. Solid understanding of SDLC and CI/CD processes. Desired : experience in Infrastructure as Code developments and tools e.g. Terraform Desired : experience with MLOps deployment and maintenance. Desired: Data Engineering technologies e.g. ETL , Spark , Dataflow , BigQuery Please note: even if you don't have exactly the background indicated, do more »
processing and exposition technologies and trade-offs Experience in the design and implementation of Data & Analytics (D&A) operation scaling process framework (DataOps and MLOps), in order to ensure the productivity, reliability and security of a Data platform. Proven experience in designing and implementing forward-thinking Data Mesh architecture models more »
MLOps Engineer (London – visit the office x3 days a week) Xcede are delighted to be working with a very exciting scale-up Tech Product organisation. With a fledgling Data Science team already in place who focus on recommendation systems and customer personalisation, the company now want to hire an MLOps … decision within the ML function – MLOps. Requirements Strong academic background in a relevant field (Statistics, Computer Science, etc.) Significant commercial experience deploying and maintaining (MLOps) Machine Learning algorithms and infrastructure. This would ideally be in a digital B2C product or platform. Some experience of deploying recommendation engines/recommender systems more »
ll be working closely with Data Scientists and Software Engineers to develop and productionise a greenfield NLP product. Experience required (min. 5 years): NLP MLOps with Cloud (AWS w/SageMaker preferred, other clouds also considered) End-to-end ML experience Other details: Up to £650 per day outside IR35 more »
close collaboration with researchers a strong plus Experience with data driven python pipelines, data/ML platform design production model evaluation, and/or MLOps a strong plus Portfolio optimisation/market impact models/optimal execution/transaction cost analysis a strong plus Experience with production front office trading more »
application. These include ingesting and understanding new data sources, evolving our data approach (e.g., to a fully fledged data fabric), implementing containers to streamline MLOps and exploring how we can sensibly and safely utilise artificial intelligence (AI) to turbo charge our work (e.g., smart data quality monitoring, meta data curation … desirable Utilising AI within data engineering to drive performance. Knowledge of implementing, managing and utilising graph databases (e.g., using technologies such as Neo4j). MLOps experience including familiarity with Docker and Kubernetes. Full data engineering cycle knowledge (tools and skills) for stream data. Benefits Competitive salary and rewards package including more »
Prometheus Graphana Cloud provided (preferably GCP but AWS and Azure are also considered) CI/CD with Jenkins or Octopus Nice to have Datadog MLOPS Salary Up to £100K basic + £112.500K RSUs + 7.5% bonus Submit your details for more information about this opportunity. RedCat Digital is acting as more »
visualization tools (e.g., Tableau). - Basic statistical analysis knowledge. - Experience in deploying production ML systems. - Familiarity with DevOps in a data science context (e.g., MLOps) is a plus. Tools currently being used; - Python3, Numpy, Scipy, Xgboost - CI/CD: GitHub Actions, Jenkins, Docker - MLOps: DVC, MLflow - BI: Terraform, Airflow, BigQuery more »
visualization tools (e.g., Tableau). - Basic statistical analysis knowledge. - Experience in deploying production ML systems. - Familiarity with DevOps in a data science context (e.g., MLOps) is a plus. Tools currently being used; - Python3, Numpy, Scipy, Xgboost - CI/CD: GitHub Actions, Jenkins, Docker - MLOps: DVC, MLflow - BI: Terraform, Airflow, BigQuery more »
Manchester, North West, United Kingdom Hybrid / WFH Options
N Brown Group
We're looking for a Lead Machine Learning Engineer join our Data Engineering team. As the Lead ML Ops Engineer, you will be at the forefront of innovation, leading a small team of skilled engineers to develop and deploy scalable more »
Who We Are Boston Consulting Group partners with leaders in business and society to tackle their most important challenges and capture their greatest opportunities. BCG was the pioneer in business strategy when it was founded in 1963. Today, we help more »
Belfast, Northern Ireland, United Kingdom Hybrid / WFH Options
Realtime Recruitment
Realtime are extremely proud to be partnering with a global tech leader on their search for an experienced Machine Learning Engineering Manager to lead their agile ML Engineering team here in Belfast. This team are providing word class machine learning more »
