7th July 2023
ML Ops Engineer
Big Data
London
£80,000 per annum
Spencer Rose are looking for a highly skilled and motivated ML Ops Data Engineer to join our clients team and spearhead the creation and implementation of ML Ops capabilities within our clients organisation. Our client is a Software-as-a-Service company operating in the Maritime industry.
As a ML Ops Data Engineer, you will be responsible for:
- Building and managing the infrastructure necessary to optimize data pipelines, streamline model deployment, and enhancing the overall efficiency of machine learning systems.
- Working closely with our internal Data Scientists and Data Engineers, you’ll help design sustainable and scalable Data
- Science driven solutions.
- Set up appropriate monitoring systems for ML models running in production
- Automate the retraining of new data sets and then working this into production
- Ensure the integrity and accuracy of training and evaluation data through data quality monitoring and
- validation processes.
- Conduct regular code reviews, implement unit tests, and ensure adherence to best practices in softwaredevelopment and ML Ops.
ML OPs Engineer Skill Requirements:
- Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field
- Proven experience as a Data Engineer or ML Ops Engineer, preferably in a cloud-based environment
- Strong knowledge/hands on experience of machine learning principles, algorithms, and frameworks
- Proficiency in Python and experience with relevant libraries such as pandas, NumPy, and Apache Spark
- Experience in designing and developing Data Engineering pipelines across different paradigms such as batch, event driven and streaming using relevant tools
- Experience with Microservice architecture, Models as a Service and RESTful APIs
- Experience in developing production-level ML applications
- Experience with DevOps/ML Ops principles
- Familiarity with containerization technologies like Docker and container orchestration frameworks like Kubernetes
- Solid understanding of software development principles, version control systems (eg, Git), and CI/CD methodologies
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