My client, a top tier global bank is looking for a Lead DevOps Engineer to join their team based in London. This is a PAYE day rate contract role.
What you will be doing:
- Contributing in designing and developing robust architecture of the Machine Learning platform that would allow to build, host and monitor ML models created by the team.
- Engagement with various stakeholders within wider function, lead data requirements for various PoC, execute the platform development plan, deploy the solution and manage the change upon implementation.
- Participate in projects leading to end-to-end cloud native solutions for predictive analytics
- Work closely with Data Scientists to provide DevOps support to their development of predictive analytics tools, their prototyping and implementation, including but not limited to Chatbot projects.
- Create Data Platform for Advanced Analytics teams to enable them in creating of end-to-end automation and AI capabilities
- Package AI/ML solutions developed by Advanced Analytics using Docker/Kubernetes and host them as microservices on cloud (GCP/AWS)
- Develop and manage CI/CD pipeline for both GCP and AWS infrastructures
- Create ETL script for sourcing and connecting data from structured and unstructured database
- Integrate new data management technologies and software engineering tools into existing structures
- Collaborate with data scientists, data architects and team members on project goals
What we are looking for:
- Post Graduate qualifications (quantitative MSc / PhD) in Software Engineering, Statistics, Applied Mathematics, Econometrics, Electronic Engineering or any other relevant quantitative discipline.
- Strong experience in creating infrastructure platforms for auto-scaling Machine Learning solutions (i.e. NLP (Natural Language Processing), RPA (Robotic Process Automation), ChatBot, etc.), experience in developing end-to-end solutions and deploying them.
- Knowledge of database architectures, ETL scripting for Hadoop-based technologies, SQL-based technologies and NoSQL technologies. Familiarity with data ingestion tools, data analysis tools such as Spark, MLflow, and workflow automation tools like Airflow and Kubeflow.
- Experience with Google Cloud Platform and services and experience with equivalent services on AWS or Microsoft Azure.
- Strong experience in at least two of the following: Python, Java, C/C++, SQL, R relevant data visualisation tools.
- Very good knowledge of Linux system.
- Experience of working in relevant Software Engineering or Analytics field. Knowledge and understanding of financial services preferred and market conduct landscape is preferred.
Please send your CV to email@example.com
Please note our advertisements use PQE/salary levels purely as a guide. However we are happy to consider applications from all candidates who are able to demonstrate the skills necessary to fulfil the role.