Machine Learning Engineer

Purpose of the job

Building the machine learning production infrastructure (or MLOps) is the biggest challenge most large companies currently have in making the transition to becoming an AI-driven organization. You will be part of a team deploying state-of-the-art ML and AI solutions for Collinson Valuedynamx . Our team of data scientists and engineers create innovative solution using ML and AI, we need to then ensure this solution is deployed successfully to PROD environment, but how should it be deployed into production? How will end users access the solution? How will it scale to processing millions of requests? What tools or platforms should the client use for monitoring? As ML DevOps engineer you will be looking for answers to all the above and in addition be able to take ownership of monitoring the performance of our already existing production data solutions.

As an innovator, you will be tasked with bringing fresh ideas to the team and continuously exploring new and modern engineering frameworks to enhance the overall offerings of the Collinson group. A key aspect of this role will also be to collaborate with the data platform team to integrate with the ML platform, and to support the growth and development of the team's ML skillset

Key Responsibilities

Design, Develop and Deliver -

Drive the development of machine learning pipelines for data-driven products and services - Contribute to architecture and technical decisions to create machine learning workflows and pipelines in cloud (e.g.AWS) - Collaborate with data scientists and engineers to deploy new machine learning and deep learning models into complex and mission critical production systems - Select the right tool(s)/services(s) for the job and make it work in production - Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients machine learning systems - Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc. - Support model development, with an emphasis on auditability, versioning, and data security - Facilitate the development and deployment of proof-of-concept machine learning systems - Communicate with clients to build requirements and track progress - Promote a culture of self-serve data analytics by minimizing technical barriers to data access and understanding. - Stay current with the latest research and technology and communicate your knowledge throughout the enterprise

Day to Day Activities will include

• Working on all stages of projects (planning, development, quality control, production)

• Design, build and ongoing maintenance of our strategic platform and tooling.

• Producing machine learning models including supervised and unsupervised methods

• Rapidly prototyping proof-of-concept idea

• Converting proof-of-concept projects to enterprise solutions

• Producing reports and presentations to communicate findings to stakeholders

• Investigate and understand emerging trends in data-related approaches, performing horizonscanning that present current and future opportunities for the business.